pSERG: Comparison of time to treatment before and after publication of awareness of delays in the pSERG consortium
Install needed packages
# install.packages("gdata")
library(gdata)
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## Attaching package: 'gdata'
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## startsWith
# install.packages("car")
library(car)
## Warning: package 'car' was built under R version 3.5.3
## Loading required package: carData
## Warning: package 'carData' was built under R version 3.5.2
# install.packages("lubridate")
library(lubridate)
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## date
# install.packages("gmodels")
library(gmodels)
# install.packages("survival")
library(survival)
# install.packages("survRM2")
library(survRM2)
## Warning: package 'survRM2' was built under R version 3.5.3
Load database
# Load database
pSERG <- read.csv("G:\\PROJECTS\\pSERGbeforeafter\\Data\\pSERG.csv")
Data cleaning and data transformation
# Keep only the cases of refractory status epilepticus
pSERG <- pSERG[pSERG$SE_GROUP=="refractory_case", ]
# Transform date of status epilepticus into date format
pSERG$DATESEIZURE <- as.Date(pSERG$DATESEIZURE, format = "%m/%d/%Y")
# Order by date of status epilepticus
pSERG <- pSERG[order(pSERG$PATIENT_LABEL, pSERG$DATESEIZURE), ]
# Delete duplicate episodes from the same patient
pSERG <- pSERG[!duplicated(pSERG$PATIENT_LABEL), ]
# Delete patients with unknown age
pSERG$G_T_STTS_PLPTCS_EPISODE_MONTHS <- as.numeric(as.character(pSERG$G_T_STTS_PLPTCS_EPISODE_MONTHS))
## Warning: NAs introduced by coercion
pSERG$G_T_STTS_PLPTCUS_EPISODE_YEARS <- as.numeric(as.character(pSERG$G_T_STTS_PLPTCUS_EPISODE_YEARS))
## Warning: NAs introduced by coercion
pSERG$ageyears <- pSERG$G_T_STTS_PLPTCUS_EPISODE_YEARS + (pSERG$G_T_STTS_PLPTCS_EPISODE_MONTHS/12)
pSERG <- pSERG[complete.cases(pSERG[ ,"ageyears"]), ]
# Delete patients with unknown sex
pSERG <- pSERG[which(pSERG$SEX == "male" | pSERG$SEX == "female"), ]
pSERG$SEX <- droplevels(pSERG$SEX)
# Delete patients with unknown hospital onset
pSERG <- pSERG[which(pSERG$HOSPITALONSET == "yes" | pSERG$HOSPITALONSET == "no"), ]
pSERG$HOSPITALONSET <- droplevels(pSERG$HOSPITALONSET)
# Transform BZDTIME.0 to numeric
pSERG$BZDTIME.0 <- as.numeric(as.character(pSERG$BZDTIME.0))
## Warning: NAs introduced by coercion
# Delete patients with unknown time to first BZD
pSERG <- pSERG[complete.cases(pSERG[ , "BZDTIME.0"]), ]
# Transform AEDTIME.0 to numeric
pSERG$AEDTIME.0 <- as.numeric(as.character(pSERG$AEDTIME.0))
## Warning: NAs introduced by coercion
# Delete patients with unknown time to first non-BZD-AED
pSERG <- pSERG[complete.cases(pSERG[ , "AEDTIME.0"]), ]
# Delete patients with unknown type of SE (continuous vs intermittent)
pSERG <- pSERG[which(pSERG$TYPESTATUS == "continuous" | pSERG$TYPESTATUS == "intermittent"), ]
pSERG$TYPESTATUS <- droplevels(pSERG$TYPESTATUS)
# Create convulsive duration in minutes and eliminate patients with unknown convulsive duration
pSERG$CONVULSIVEDURATION <- as.numeric(as.character(pSERG$CONVULSIVEDURATION))
## Warning: NAs introduced by coercion
pSERG$CONVULSIVEmin <- pSERG$CONVULSIVEDURATION
pSERG$CONVULSIVEhr <- pSERG$CONVULSIVEDURATION * 60
pSERG$convulsivedurationmin <- ifelse(pSERG$CONVULSIVEDURATIONUNITS=="min", pSERG$CONVULSIVEmin, pSERG$CONVULSIVEhr)
pSERG <- pSERG[complete.cases(pSERG[ , "convulsivedurationmin"]), ]
# Delete patients with unknown race
pSERG <- pSERG[complete.cases(pSERG[ , "RACE"]), ]
pSERG$RACE <- droplevels(pSERG$RACE)
# Delete patients with unknown or nonsensical time of SE
pSERG$TIMESEIZURE_HOURS <- as.numeric(as.character(pSERG$TIMESEIZURE_HOURS))
## Warning: NAs introduced by coercion
pSERG <- pSERG[complete.cases(pSERG[ ,"TIMESEIZURE_HOURS"]), ]
pSERG$TIMESEIZURE_HOURS <- ifelse(pSERG$TIMESEIZURE_HOURS >= 100, pSERG$TIMESEIZURE_HOURS/100, pSERG$TIMESEIZURE_HOURS)
# Create variable day/night
pSERG$day <- ifelse(pSERG$TIMESEIZURE_HOURS >= 8 & pSERG$TIMESEIZURE_HOURS < 20, 1, 0)
# Delete patients with unknown time of SE onset (day or night)
pSERG <- pSERG[complete.cases(pSERG[ , "day"]), ]
###############VARIABLE CREATION#########################
# Divide race into White and non-white
pSERG$white <- ifelse(pSERG$RACE == 'white', 1, 0)
# Create variable delay
pSERG$delay[grepl("delay", pSERG$PAST)] <- 1
pSERG$delay[!grepl("delay", pSERG$PAST)] <- 0
# Create variable cerebral palsy
pSERG$palsy[grepl("palsy", pSERG$PAST)] <- 1
pSERG$palsy[!grepl("palsy", pSERG$PAST)] <- 0
# Create variable febrile
pSERG$febrile[grepl("febrile", pSERG$PAST)] <- 1
pSERG$febrile[!grepl("febrile", pSERG$PAST)] <- 0
# Create variable none (no neurological comorbidities)
pSERG$none[grepl("none", pSERG$PAST)] <- 1
pSERG$none[!grepl("none", pSERG$PAST)] <- 0
# Create variable prior epilepsy
pSERG$priorepilepsy[grepl("epi",pSERG$PAST)] <- 1
pSERG$priorepilepsy[!grepl("epi",pSERG$PAST)] <- 0
# Create variable prior status
pSERG$status[grepl("status",pSERG$PAST)] <- 1
pSERG$status[!grepl("status",pSERG$PAST)] <- 0
# Create variable of at least one continuous infusion
pSERG$CI <- ifelse(!(pSERG$CONTMED.0==""), 1, 0)
# Transform CI time into numeric
pSERG$CONTTIME.0 <- as.numeric(as.character(pSERG$CONTTIME.0))
## Warning: NAs introduced by coercion
# Create ICU stay in days
pSERG$ICU_DURATION <- as.numeric(as.character(pSERG$ICU_DURATION))
## Warning: NAs introduced by coercion
pSERG$ICUhours <- pSERG$ICU_DURATION/24
pSERG$ICUdays <- pSERG$ICU_DURATION
pSERG$ICUdurationdays <- ifelse(pSERG$ICU_UNITS=="days", pSERG$ICUdays, pSERG$ICUhours)
# Transform EMS arrival to numeric
pSERG$EMSARRIVAL <- as.numeric(as.character(pSERG$EMSARRIVAL))
## Warning: NAs introduced by coercion
# Transform time to hospital arrival to numeric
pSERG$HOSPITALARRIVAL <- as.numeric(as.character(pSERG$HOSPITALARRIVAL))
## Warning: NAs introduced by coercion
# Create variable BZD before hospital arrival
pSERG$AEDbeforehospital <- ifelse(pSERG$BZDTIME.0 < pSERG$HOSPITALARRIVAL, 1, 0)
# Reclasify etiology
pSERG$etiology2 <- recode(pSERG$ETIOLOGY,
"'genetic' = 'genetic';
'metabolic'= 'metabolic';
'other' = 'other';
'structural' = 'structural';
'unknown' = 'unknown';
'' = 'unknown'")
# Structural etiology
pSERG$structuraletiology <- ifelse(pSERG$etiology2 == "structural", 1, 0)
# Create variable early academic year
pSERG$dateSE <- as.POSIXct(pSERG$DATESEIZURE, format = "%m/%d/%Y")
pSERG$monthSE <- month(pSERG$dateSE, label = FALSE)
pSERG$earlyacademicyear <- ifelse(pSERG$monthSE >= 7 & pSERG$monthSE <= 12, 1, 0)
# Create variable awareness of delays in time to treatment
pSERG$yearSE <- year(pSERG$dateSE)
pSERG$awareness <- ifelse(pSERG$yearSE >= 2015, 1, 0)
# Create variable event to use functions related to censoring
pSERG$event <- 1
# Create variable firstBZDmore20min
pSERG$firstBZDmore20min <- as.factor(ifelse(pSERG$BZDTIME.0 > 20, 1, 0))
# Create variable firstBZDmore40min
pSERG$firstBZDmore40min <- as.factor(ifelse(pSERG$BZDTIME.0 > 40, 1, 0))
# Create variable firstBZDmore60min
pSERG$firstBZDmore60min <- as.factor(ifelse(pSERG$BZDTIME.0 > 60, 1, 0))
# Create variable firstASMmore40min
pSERG$firstASMmore40min <- as.factor(ifelse(pSERG$AEDTIME.0 > 40, 1, 0))
# Create variable firstASMmore60min
pSERG$firstASMmore60min <- as.factor(ifelse(pSERG$AEDTIME.0 > 60, 1, 0))
# Create variable firstASMmore120min
pSERG$firstASMmore120min <- as.factor(ifelse(pSERG$AEDTIME.0 > 120, 1, 0))
# Create variable firstCImore60min
pSERG$firstCImore60min <- as.factor(ifelse(pSERG$CONTTIME.0 > 60, 1, 0))
# Create variable firstCImore120min
pSERG$firstCImore120min <- as.factor(ifelse(pSERG$CONTTIME.0 > 120, 1, 0))
# Create variable firstCImore240min
pSERG$firstCImore240min <- as.factor(ifelse(pSERG$CONTTIME.0 > 240, 1, 0))
# Transform variables to numeric for the rmst2 function
pSERG$TYPESTATUSnumeric <- ifelse(pSERG$TYPESTATUS=="continuous", 1, 0)
pSERG$HOSPITALONSETnumeric <- ifelse(pSERG$HOSPITALONSET=="yes", 1, 0)
pSERG$SEXnumeric <- ifelse(pSERG$SEX=="male", 1, 0)
Demographics
# Proportion of patients with rSE during the period of increased awareness of delays in time to treatment
CrossTable(pSERG$awareness)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 151 | 177 |
## | 0.460 | 0.540 |
## |-----------|-----------|
##
##
##
##
# Age
nobs(pSERG$ageyears)
## [1] 328
summary(pSERG$ageyears)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.08333 1.25419 3.83333 5.75898 9.35417 20.74167
sd(pSERG$ageyears)
## [1] 5.207827
# Age in patients <2015
nobs(pSERG[pSERG$awareness==0, ]$ageyears)
## [1] 151
summary(pSERG[pSERG$awareness==0, ]$ageyears)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.125 1.167 3.750 5.616 8.863 20.742
sd(pSERG[pSERG$awareness==0, ]$ageyears)
## [1] 5.290925
# Age in patients >=2015
nobs(pSERG[pSERG$awareness==1, ]$ageyears)
## [1] 177
summary(pSERG[pSERG$awareness==1, ]$ageyears)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.08333 1.28583 4.08333 5.88063 9.63608 19.30583
sd(pSERG[pSERG$awareness==1, ]$ageyears)
## [1] 5.147784
# Sex
CrossTable(pSERG$SEX)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
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##
## Total Observations in Table: 328
##
##
## | female | male |
## |-----------|-----------|
## | 145 | 183 |
## | 0.442 | 0.558 |
## |-----------|-----------|
##
##
##
##
# Sex in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$SEX)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | female | male |
## |-----------|-----------|
## | 71 | 80 |
## | 0.470 | 0.530 |
## |-----------|-----------|
##
##
##
##
# Sex in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$SEX)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | female | male |
## |-----------|-----------|
## | 74 | 103 |
## | 0.418 | 0.582 |
## |-----------|-----------|
##
##
##
##
# Race
CrossTable(pSERG$RACE)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | american_indian_alaska_native | arabic | asian | black_or_african_american | native_hawaiian_or_pacific_islander |
## |-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|
## | 1 | 10 | 11 | 65 | 2 |
## | 0.003 | 0.030 | 0.034 | 0.198 | 0.006 |
## |-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|
##
##
## | not_reported | unknown | white |
## |-------------------------------------|-------------------------------------|-------------------------------------|
## | 8 | 22 | 209 |
## | 0.024 | 0.067 | 0.637 |
## |-------------------------------------|-------------------------------------|-------------------------------------|
##
##
##
##
# Race in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$RACE)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | arabic | asian | black_or_african_american | native_hawaiian_or_pacific_islander | not_reported |
## |-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|
## | 7 | 6 | 38 | 1 | 5 |
## | 0.046 | 0.040 | 0.252 | 0.007 | 0.033 |
## |-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|
##
##
## | unknown | white |
## |-------------------------------------|-------------------------------------|
## | 13 | 81 |
## | 0.086 | 0.536 |
## |-------------------------------------|-------------------------------------|
##
##
##
##
# Race in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$RACE)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | american_indian_alaska_native | arabic | asian | black_or_african_american | native_hawaiian_or_pacific_islander |
## |-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|
## | 1 | 3 | 5 | 27 | 1 |
## | 0.006 | 0.017 | 0.028 | 0.153 | 0.006 |
## |-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|-------------------------------------|
##
##
## | not_reported | unknown | white |
## |-------------------------------------|-------------------------------------|-------------------------------------|
## | 3 | 9 | 128 |
## | 0.017 | 0.051 | 0.723 |
## |-------------------------------------|-------------------------------------|-------------------------------------|
##
##
##
##
# Ethnicity
CrossTable(pSERG$ETHNICITY)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | hispanic_or_latino | not_hispanic_or_latino | not_reported | unknown |
## |------------------------|------------------------|------------------------|------------------------|
## | 51 | 247 | 18 | 12 |
## | 0.155 | 0.753 | 0.055 | 0.037 |
## |------------------------|------------------------|------------------------|------------------------|
##
##
##
##
# Ethnicity in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$ETHNICITY)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | hispanic_or_latino | not_hispanic_or_latino | not_reported | unknown |
## |------------------------|------------------------|------------------------|------------------------|
## | 26 | 111 | 8 | 6 |
## | 0.172 | 0.735 | 0.053 | 0.040 |
## |------------------------|------------------------|------------------------|------------------------|
##
##
##
##
# Ethnicity in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$ETHNICITY)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | hispanic_or_latino | not_hispanic_or_latino | not_reported | unknown |
## |------------------------|------------------------|------------------------|------------------------|
## | 25 | 136 | 10 | 6 |
## | 0.141 | 0.768 | 0.056 | 0.034 |
## |------------------------|------------------------|------------------------|------------------------|
##
##
##
##
# Delay
CrossTable(pSERG$delay)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 161 | 167 |
## | 0.491 | 0.509 |
## |-----------|-----------|
##
##
##
##
# Delay in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$delay)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 75 | 76 |
## | 0.497 | 0.503 |
## |-----------|-----------|
##
##
##
##
# Delay in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$delay)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 86 | 91 |
## | 0.486 | 0.514 |
## |-----------|-----------|
##
##
##
##
# Prior epilepsy
CrossTable(pSERG$priorepilepsy)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 167 | 161 |
## | 0.509 | 0.491 |
## |-----------|-----------|
##
##
##
##
# Prior epilepsy in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$priorepilepsy)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 80 | 71 |
## | 0.530 | 0.470 |
## |-----------|-----------|
##
##
##
##
# Prior epilepsy in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$priorepilepsy)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 87 | 90 |
## | 0.492 | 0.508 |
## |-----------|-----------|
##
##
##
##
# Status
CrossTable(pSERG$status)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 266 | 62 |
## | 0.811 | 0.189 |
## |-----------|-----------|
##
##
##
##
# Status in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$status)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 125 | 26 |
## | 0.828 | 0.172 |
## |-----------|-----------|
##
##
##
##
# Status in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$status)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 141 | 36 |
## | 0.797 | 0.203 |
## |-----------|-----------|
##
##
##
##
# Palsy
CrossTable(pSERG$palsy)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 297 | 31 |
## | 0.905 | 0.095 |
## |-----------|-----------|
##
##
##
##
# Palsy in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$palsy)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 138 | 13 |
## | 0.914 | 0.086 |
## |-----------|-----------|
##
##
##
##
# Palsy in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$palsy)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 159 | 18 |
## | 0.898 | 0.102 |
## |-----------|-----------|
##
##
##
##
# None
CrossTable(pSERG$none)
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##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 218 | 110 |
## | 0.665 | 0.335 |
## |-----------|-----------|
##
##
##
##
# None in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$none)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 95 | 56 |
## | 0.629 | 0.371 |
## |-----------|-----------|
##
##
##
##
# None in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$none)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 123 | 54 |
## | 0.695 | 0.305 |
## |-----------|-----------|
##
##
##
##
# Etiology
CrossTable(pSERG$etiology2)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | genetic | metabolic | other | structural | unknown |
## |------------|------------|------------|------------|------------|
## | 62 | 15 | 56 | 83 | 112 |
## | 0.189 | 0.046 | 0.171 | 0.253 | 0.341 |
## |------------|------------|------------|------------|------------|
##
##
##
##
# Etiology in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$etiology2)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | genetic | metabolic | other | structural | unknown |
## |------------|------------|------------|------------|------------|
## | 24 | 11 | 25 | 44 | 47 |
## | 0.159 | 0.073 | 0.166 | 0.291 | 0.311 |
## |------------|------------|------------|------------|------------|
##
##
##
##
# Etiology in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$etiology2)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | genetic | metabolic | other | structural | unknown |
## |------------|------------|------------|------------|------------|
## | 38 | 4 | 31 | 39 | 65 |
## | 0.215 | 0.023 | 0.175 | 0.220 | 0.367 |
## |------------|------------|------------|------------|------------|
##
##
##
##
# Hospital onset
CrossTable(pSERG$HOSPITALONSET)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | no | yes |
## |-----------|-----------|
## | 222 | 106 |
## | 0.677 | 0.323 |
## |-----------|-----------|
##
##
##
##
# Hospital onset in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$HOSPITALONSET)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | no | yes |
## |-----------|-----------|
## | 105 | 46 |
## | 0.695 | 0.305 |
## |-----------|-----------|
##
##
##
##
# Hospital onset in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$HOSPITALONSET)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | no | yes |
## |-----------|-----------|
## | 117 | 60 |
## | 0.661 | 0.339 |
## |-----------|-----------|
##
##
##
##
# Convulsive duration
nobs(pSERG$convulsivedurationmin)
## [1] 328
summary(pSERG$convulsivedurationmin)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 60.0 124.5 2159.7 281.5 172800.0
# Convulsive duration in patients <2015
summary(pSERG[pSERG$awareness==0, ]$convulsivedurationmin)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 60 135 2582 275 172800
# Convulsive duration in patients >=2015
summary(pSERG[pSERG$awareness==1, ]$convulsivedurationmin)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 60 120 1800 286 90720
# Time to first BZD
nobs(pSERG$BZDTIME.0)
## [1] 328
summary(pSERG$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 17.00 63.74 45.00 1440.00
# Time to first BZD in patients <2015
summary(pSERG[pSERG$awareness==0, ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 20.00 55.26 52.50 720.00
# Time to first BZD in patients >=2015
summary(pSERG[pSERG$awareness==1, ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 15.00 70.98 38.00 1440.00
# Time to first non-BZD-AED
nobs(pSERG$AEDTIME.0)
## [1] 328
summary(pSERG$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 33.75 65.50 162.60 150.00 4320.00
# Time to first non-BZD-AED in patients <2015
summary(pSERG[pSERG$awareness==0, ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.0 34.5 68.0 155.9 163.5 1800.0
# Time to first non-BZD-AED in patients >=2015
summary(pSERG[pSERG$awareness==1, ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 33.0 65.0 168.3 142.0 4320.0
# Time to first CI
nobs(pSERG$CONTTIME.0)
## [1] 152
summary(pSERG$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 113.5 173.5 523.7 543.2 7200.0 176
# Time to first CI in patients <2015
summary(pSERG[pSERG$awareness==0, ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 124.2 186.0 511.8 571.0 4320.0 83
# Time to first CI in patients >=2015
summary(pSERG[pSERG$awareness==1, ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 89.5 160.0 533.4 495.0 7200.0 93
# Length of stay
nobs(pSERG$ICUdurationdays)
## [1] 311
summary(pSERG$ICUdurationdays)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 2.00 4.00 12.15 11.00 180.00 17
# Length of stay in patients <2015
summary(pSERG[pSERG$awareness==0, ]$ICUdurationdays)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 4.278 11.912 11.932 154.000 2
# Length of stay in patients >=2015
summary(pSERG[pSERG$awareness==1, ]$ICUdurationdays)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.691 4.000 12.368 11.000 180.000 15
# Mortality
CrossTable(pSERG$ALIVE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | | no | yes |
## |-----------|-----------|-----------|
## | 3 | 11 | 314 |
## | 0.009 | 0.034 | 0.957 |
## |-----------|-----------|-----------|
##
##
##
##
# Mortality in patients <2015
CrossTable(pSERG[pSERG$awareness==0, ]$ALIVE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | no | yes |
## |-----------|-----------|
## | 5 | 146 |
## | 0.033 | 0.967 |
## |-----------|-----------|
##
##
##
##
# Mortality in patients >=2015
CrossTable(pSERG[pSERG$awareness==1, ]$ALIVE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | | no | yes |
## |-----------|-----------|-----------|
## | 3 | 6 | 168 |
## | 0.017 | 0.034 | 0.949 |
## |-----------|-----------|-----------|
##
##
##
##
# Time to first BZD by year
tapply(pSERG$BZDTIME.0, as.factor(pSERG$yearSE), length)
## 2011 2012 2013 2014 2015 2016 2017 2018 2019
## 2 59 44 46 36 50 42 34 15
tapply(pSERG$BZDTIME.0, as.factor(pSERG$yearSE), summary)
## $`2011`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5 5 5 5 5 5
##
## $`2012`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 5.00 17.00 43.71 60.00 360.00
##
## $`2013`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 20.00 67.07 49.25 538.00
##
## $`2014`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 18.50 60.96 53.75 720.00
##
## $`2015`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 14.00 71.42 29.25 1264.00
##
## $`2016`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 8.25 20.00 63.88 49.50 1132.00
##
## $`2017`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 5.00 16.50 89.29 41.00 1440.00
##
## $`2018`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 6.00 15.00 66.12 32.25 625.00
##
## $`2019`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 5.00 22.00 53.33 32.50 517.00
# Figure time to first BZD by year
plot(survfit(Surv(pSERG$BZDTIME.0) ~ as.factor(pSERG$yearSE)), fun = "event",
conf.int = FALSE, xlim = c(0,60),
col = c("aquamarine2", "chartreuse", "darkorchid1", "darkorange", "brown3", "cyan2", "deeppink1", "coral2", "darkturquoise"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("bottomright", legend=c("2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"),
col=c("aquamarine2", "chartreuse", "darkorchid1", "darkorange", "brown3", "cyan2", "deeppink1", "coral2", "darkturquoise"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1)
# Time to first ASM by year
tapply(pSERG$AEDTIME.0, as.factor(pSERG$yearSE), length)
## 2011 2012 2013 2014 2015 2016 2017 2018 2019
## 2 59 44 46 36 50 42 34 15
tapply(pSERG$AEDTIME.0, as.factor(pSERG$yearSE), summary)
## $`2011`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 30.00 45.75 61.50 61.50 77.25 93.00
##
## $`2012`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 28.0 61.0 105.0 112.5 780.0
##
## $`2013`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.0 39.0 65.0 174.5 184.0 1800.0
##
## $`2014`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 20.0 48.5 83.0 207.5 207.5 1440.0
##
## $`2015`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 31.5 62.5 253.1 150.0 4320.0
##
## $`2016`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 42.75 78.00 129.76 129.75 1276.00
##
## $`2017`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 30.5 63.0 151.5 118.8 1419.0
##
## $`2018`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.00 34.25 63.00 108.97 119.50 385.00
##
## $`2019`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 40.5 77.0 274.7 378.0 1488.0
# Figure time to first non-BZD AED by year
plot(survfit(Surv(pSERG$AEDTIME.0) ~ as.factor(pSERG$yearSE)), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("aquamarine2", "chartreuse", "darkorchid1", "darkorange", "brown3", "cyan2", "deeppink1", "coral2", "darkturquoise"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("bottomright", legend=c("2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"),
col=c("aquamarine2", "chartreuse", "darkorchid1", "darkorange", "brown3", "cyan2", "deeppink1", "coral2", "darkturquoise"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1)
# Time to first CI by year
tapply(pSERG$CONTTIME.0, as.factor(pSERG$yearSE), length)
## 2011 2012 2013 2014 2015 2016 2017 2018 2019
## 2 59 44 46 36 50 42 34 15
tapply(pSERG$CONTTIME.0, as.factor(pSERG$yearSE), summary)
## $`2011`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 360 360 360 360 360 360 1
##
## $`2012`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 30.0 125.0 180.0 588.2 520.0 4320.0 38
##
## $`2013`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 148.0 230.0 411.7 525.0 2880.0 19
##
## $`2014`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20 102 165 562 992 2520 25
##
## $`2015`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 45.0 118.0 166.0 756.5 367.5 7200.0 20
##
## $`2016`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 53.0 103.5 160.0 338.2 440.5 1470.0 19
##
## $`2017`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.00 71.75 157.50 446.95 505.00 2370.00 22
##
## $`2018`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 49.00 85.75 108.00 382.71 219.50 3008.00 20
##
## $`2019`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 480.0 957.5 1435.0 2639.3 3719.0 6003.0 12
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG$CONTTIME.0) ~ as.factor(pSERG$yearSE)), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("aquamarine2", "chartreuse", "darkorchid1", "darkorange", "brown3", "cyan2", "deeppink1", "coral2", "darkturquoise"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("bottomright", legend=c("2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019"),
col=c("aquamarine2", "chartreuse", "darkorchid1", "darkorange", "brown3", "cyan2", "deeppink1", "coral2", "darkturquoise"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
Time to treatment
# Time to first BZD
summary(pSERG$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 17.00 63.74 45.00 1440.00
sd(pSERG$BZDTIME.0)
## [1] 157.5196
survfit(Surv(pSERG$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$BZDTIME.0) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## 328 328 17 14 20
# Figure time to first BZD
plot(survfit(Surv(pSERG$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$awareness == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 20.00 55.26 52.50 720.00
summary(pSERG[which(pSERG$awareness == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 15.00 70.98 38.00 1440.00
survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho = 1)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 151 76.8 81.3 0.245 0.733
## pSERG$awareness=1 177 94.1 89.6 0.223 0.733
##
## Chisq= 0.7 on 1 degrees of freedom, p= 0.4
pchisq(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.3918653
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$BZDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS +
## pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 328, number of events= 328
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness 0.012427 1.012505 0.113800 0.109 0.91304
## pSERG$TYPESTATUSintermittent -0.400432 0.670031 0.128309 -3.121 0.00180
## pSERG$HOSPITALONSETyes 0.362862 1.437438 0.124163 2.922 0.00347
## pSERG$day 0.094270 1.098856 0.114743 0.822 0.41132
## pSERG$earlyacademicyear 0.191032 1.210498 0.112253 1.702 0.08879
## pSERG$white 0.001637 1.001639 0.122804 0.013 0.98936
## pSERG$structuraletiology 0.059220 1.061008 0.135405 0.437 0.66186
## pSERG$priorepilepsy 0.021959 1.022202 0.124602 0.176 0.86011
## pSERG$status 0.496292 1.642619 0.158110 3.139 0.00170
## pSERG$ageyears -0.002526 0.997477 0.011114 -0.227 0.82019
## pSERG$SEXmale 0.062825 1.064840 0.115626 0.543 0.58690
##
## pSERG$awareness
## pSERG$TYPESTATUSintermittent **
## pSERG$HOSPITALONSETyes **
## pSERG$day
## pSERG$earlyacademicyear .
## pSERG$white
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status **
## pSERG$ageyears
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness 1.0125 0.9876 0.8101 1.2655
## pSERG$TYPESTATUSintermittent 0.6700 1.4925 0.5210 0.8616
## pSERG$HOSPITALONSETyes 1.4374 0.6957 1.1269 1.8335
## pSERG$day 1.0989 0.9100 0.8775 1.3760
## pSERG$earlyacademicyear 1.2105 0.8261 0.9714 1.5084
## pSERG$white 1.0016 0.9984 0.7874 1.2742
## pSERG$structuraletiology 1.0610 0.9425 0.8137 1.3835
## pSERG$priorepilepsy 1.0222 0.9783 0.8007 1.3050
## pSERG$status 1.6426 0.6088 1.2049 2.2393
## pSERG$ageyears 0.9975 1.0025 0.9760 1.0194
## pSERG$SEXmale 1.0648 0.9391 0.8489 1.3357
##
## Concordance= 0.615 (se = 0.02 )
## Rsquare= 0.106 (max possible= 1 )
## Likelihood ratio test= 36.63 on 11 df, p=1e-04
## Wald test = 38.54 on 11 df, p=6e-05
## Score (logrank) test = 39.46 on 11 df, p=4e-05
# Time to first non-BZD AED
summary(pSERG$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 33.75 65.50 162.60 150.00 4320.00
sd(pSERG$AEDTIME.0)
## [1] 333.9342
survfit(Surv(pSERG$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$AEDTIME.0) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## 328.0 328.0 65.5 60.0 77.0
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$awareness == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.0 34.5 68.0 155.9 163.5 1800.0
summary(pSERG[which(pSERG$awareness == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 33.0 65.0 168.3 142.0 4320.0
survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho = 1)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 151 75.4 77.2 0.0412 0.117
## pSERG$awareness=1 177 90.4 88.6 0.0359 0.117
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
pchisq(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.7328349
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$AEDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS +
## pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 328, number of events= 328
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness 0.01269 1.01277 0.11358 0.112 0.9111
## pSERG$TYPESTATUSintermittent -0.56309 0.56945 0.12731 -4.423 9.73e-06
## pSERG$HOSPITALONSETyes 0.67761 1.96916 0.12243 5.535 3.12e-08
## pSERG$day 0.23887 1.26981 0.11599 2.059 0.0395
## pSERG$earlyacademicyear 0.15103 1.16303 0.11373 1.328 0.1842
## pSERG$white -0.03675 0.96392 0.11990 -0.306 0.7593
## pSERG$structuraletiology 0.27808 1.32060 0.13247 2.099 0.0358
## pSERG$priorepilepsy -0.06798 0.93428 0.12420 -0.547 0.5841
## pSERG$status 0.28074 1.32411 0.15700 1.788 0.0737
## pSERG$ageyears -0.02287 0.97739 0.01107 -2.066 0.0388
## pSERG$SEXmale 0.07086 1.07343 0.11609 0.610 0.5416
##
## pSERG$awareness
## pSERG$TYPESTATUSintermittent ***
## pSERG$HOSPITALONSETyes ***
## pSERG$day *
## pSERG$earlyacademicyear
## pSERG$white
## pSERG$structuraletiology *
## pSERG$priorepilepsy
## pSERG$status .
## pSERG$ageyears *
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness 1.0128 0.9874 0.8106 1.2653
## pSERG$TYPESTATUSintermittent 0.5694 1.7561 0.4437 0.7308
## pSERG$HOSPITALONSETyes 1.9692 0.5078 1.5491 2.5032
## pSERG$day 1.2698 0.7875 1.0116 1.5939
## pSERG$earlyacademicyear 1.1630 0.8598 0.9306 1.4535
## pSERG$white 0.9639 1.0374 0.7620 1.2193
## pSERG$structuraletiology 1.3206 0.7572 1.0186 1.7121
## pSERG$priorepilepsy 0.9343 1.0703 0.7324 1.1918
## pSERG$status 1.3241 0.7552 0.9734 1.8012
## pSERG$ageyears 0.9774 1.0231 0.9564 0.9988
## pSERG$SEXmale 1.0734 0.9316 0.8550 1.3477
##
## Concordance= 0.653 (se = 0.019 )
## Rsquare= 0.183 (max possible= 1 )
## Likelihood ratio test= 66.44 on 11 df, p=6e-10
## Wald test = 68.08 on 11 df, p=3e-10
## Score (logrank) test = 69.55 on 11 df, p=1e-10
# Time to first CI
summary(pSERG$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 113.5 173.5 523.7 543.2 7200.0 176
sd(pSERG$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$CONTTIME.0) ~ 1)
##
## 176 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 152 152 174 154 230
# Figure time to first CI
plot(survfit(Surv(pSERG$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$awareness == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 124.2 186.0 511.8 571.0 4320.0 83
summary(pSERG[which(pSERG$awareness == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 89.5 160.0 533.4 495.0 7200.0 93
survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$CONTTIME.0) ~ pSERG$awareness,
## rho = 1)
##
## n=152, 176 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 68 32.2 36.5 0.516 1.48
## pSERG$awareness=1 84 44.6 40.3 0.468 1.48
##
## Chisq= 1.5 on 1 degrees of freedom, p= 0.2
pchisq(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.2236165
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$CONTTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS +
## pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 152, number of events= 152
## (176 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness 0.079430 1.082670 0.171255 0.464 0.6428
## pSERG$TYPESTATUSintermittent -0.296428 0.743469 0.195778 -1.514 0.1300
## pSERG$HOSPITALONSETyes 0.103818 1.109398 0.183383 0.566 0.5713
## pSERG$day -0.022113 0.978130 0.175102 -0.126 0.8995
## pSERG$earlyacademicyear 0.250987 1.285293 0.178375 1.407 0.1594
## pSERG$white -0.352717 0.702776 0.193912 -1.819 0.0689
## pSERG$structuraletiology 0.212484 1.236746 0.206259 1.030 0.3029
## pSERG$priorepilepsy 0.211229 1.235195 0.199131 1.061 0.2888
## pSERG$status 0.117848 1.125073 0.231835 0.508 0.6112
## pSERG$ageyears -0.001698 0.998303 0.016895 -0.101 0.9199
## pSERG$SEXmale 0.178052 1.194888 0.173236 1.028 0.3040
##
## pSERG$awareness
## pSERG$TYPESTATUSintermittent
## pSERG$HOSPITALONSETyes
## pSERG$day
## pSERG$earlyacademicyear
## pSERG$white .
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status
## pSERG$ageyears
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness 1.0827 0.9236 0.7740 1.514
## pSERG$TYPESTATUSintermittent 0.7435 1.3450 0.5065 1.091
## pSERG$HOSPITALONSETyes 1.1094 0.9014 0.7744 1.589
## pSERG$day 0.9781 1.0224 0.6940 1.379
## pSERG$earlyacademicyear 1.2853 0.7780 0.9061 1.823
## pSERG$white 0.7028 1.4229 0.4806 1.028
## pSERG$structuraletiology 1.2367 0.8086 0.8255 1.853
## pSERG$priorepilepsy 1.2352 0.8096 0.8361 1.825
## pSERG$status 1.1251 0.8888 0.7142 1.772
## pSERG$ageyears 0.9983 1.0017 0.9658 1.032
## pSERG$SEXmale 1.1949 0.8369 0.8509 1.678
##
## Concordance= 0.569 (se = 0.028 )
## Rsquare= 0.071 (max possible= 1 )
## Likelihood ratio test= 11.21 on 11 df, p=0.4
## Wald test = 11.41 on 11 df, p=0.4
## Score (logrank) test = 11.48 on 11 df, p=0.4
## Correction for multiple comparisons
timetotreatment <- c(pchisq(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE),
summary(coxph(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS +
pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))$coefficients[1, 5],
pchisq(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE),
summary(coxph(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS +
pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy + pSERG$status +
pSERG$ageyears + pSERG$SEX))$coefficients[1, 5],
pchisq(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE),
summary(coxph(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS +
pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy + pSERG$status +
pSERG$ageyears + pSERG$SEX))$coefficients[1, 5]
)
p.adjust(timetotreatment, "BH")
## [1] 0.9130407 0.9130407 0.9130407 0.9130407 0.9130407 0.9130407
Treatment within recommendations and outliers
# First BZD later than 20 minutes
CrossTable(pSERG$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 187 | 141 |
## | 0.570 | 0.430 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 82 | 69 |
## | 0.543 | 0.457 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 105 | 72 |
## | 0.593 | 0.407 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore20min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore20min and pSERG$awareness
## p-value = 0.3728
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5132229 1.2943583
## sample estimates:
## odds ratio
## 0.8154231
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=20,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.341 -1.889 1.206 0.665
## RMST (arm=1)/(arm=0) 0.976 0.869 1.095 0.676
## RMTL (arm=1)/(arm=0) 1.058 0.834 1.341 0.643
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 16.359 1.238 13.218 0.000 13.933 18.784
## arm -0.341 0.789 -0.432 0.665 -1.889 1.206
## TYPESTATUSnumeric -0.471 0.806 -0.585 0.559 -2.051 1.108
## HOSPITALONSETnumeric -3.339 0.862 -3.873 0.000 -5.029 -1.649
## day -0.738 0.777 -0.950 0.342 -2.261 0.784
## earlyacademicyear -1.189 0.777 -1.531 0.126 -2.711 0.333
## white -0.007 0.804 -0.009 0.993 -1.584 1.569
## structuraletiology -0.689 0.927 -0.744 0.457 -2.506 1.127
## priorepilepsy -0.664 0.837 -0.794 0.427 -2.304 0.976
## status -3.154 1.139 -2.770 0.006 -5.385 -0.922
## ageyears 0.045 0.078 0.572 0.567 -0.109 0.199
## SEXnumeric 0.434 0.791 0.549 0.583 -1.115 1.984
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.812 0.092 30.609 0.000 16.639 13.897
## arm -0.025 0.059 -0.418 0.676 0.976 0.869
## TYPESTATUSnumeric -0.033 0.061 -0.539 0.590 0.968 0.859
## HOSPITALONSETnumeric -0.266 0.073 -3.647 0.000 0.767 0.665
## day -0.057 0.058 -0.993 0.321 0.944 0.843
## earlyacademicyear -0.087 0.059 -1.490 0.136 0.916 0.817
## white -0.002 0.060 -0.033 0.974 0.998 0.887
## structuraletiology -0.052 0.071 -0.731 0.465 0.949 0.825
## priorepilepsy -0.053 0.061 -0.869 0.385 0.949 0.842
## status -0.264 0.101 -2.613 0.009 0.768 0.630
## ageyears 0.003 0.006 0.582 0.561 1.003 0.992
## SEXnumeric 0.033 0.059 0.563 0.574 1.034 0.920
## upper .95
## intercept 19.921
## arm 1.095
## TYPESTATUSnumeric 1.091
## HOSPITALONSETnumeric 0.884
## day 1.057
## earlyacademicyear 1.028
## white 1.123
## structuraletiology 1.092
## priorepilepsy 1.069
## status 0.936
## ageyears 1.015
## SEXnumeric 1.161
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.415 0.197 7.178 0.000 4.116 2.797
## arm 0.056 0.121 0.463 0.643 1.058 0.834
## TYPESTATUSnumeric 0.084 0.120 0.697 0.486 1.087 0.859
## HOSPITALONSETnumeric 0.465 0.119 3.913 0.000 1.592 1.261
## day 0.104 0.121 0.862 0.389 1.110 0.876
## earlyacademicyear 0.186 0.118 1.577 0.115 1.205 0.956
## white -0.006 0.122 -0.049 0.961 0.994 0.782
## structuraletiology 0.101 0.134 0.757 0.449 1.107 0.851
## priorepilepsy 0.089 0.138 0.642 0.521 1.093 0.833
## status 0.404 0.146 2.761 0.006 1.499 1.125
## ageyears -0.007 0.012 -0.545 0.586 0.993 0.969
## SEXnumeric -0.062 0.121 -0.513 0.608 0.940 0.742
## upper .95
## intercept 6.057
## arm 1.341
## TYPESTATUSnumeric 1.376
## HOSPITALONSETnumeric 2.010
## day 1.407
## earlyacademicyear 1.519
## white 1.264
## structuraletiology 1.438
## priorepilepsy 1.433
## status 1.997
## ageyears 1.018
## SEXnumeric 1.191
# First BZD later than 40 minutes
CrossTable(pSERG$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 238 | 90 |
## | 0.726 | 0.274 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 104 | 47 |
## | 0.689 | 0.311 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 134 | 43 |
## | 0.757 | 0.243 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore40min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore40min and pSERG$awareness
## p-value = 0.1743
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4237375 1.1895374
## sample estimates:
## odds ratio
## 0.7108269
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=40,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.733 -4.913 1.447 0.286
## RMST (arm=1)/(arm=0) 0.922 0.789 1.079 0.313
## RMTL (arm=1)/(arm=0) 1.098 0.932 1.292 0.263
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 26.130 2.500 10.451 0.000 21.230 31.030
## arm -1.733 1.623 -1.068 0.286 -4.913 1.447
## TYPESTATUSnumeric -3.819 1.558 -2.451 0.014 -6.874 -0.765
## HOSPITALONSETnumeric -7.114 1.651 -4.309 0.000 -10.350 -3.878
## day -0.914 1.612 -0.567 0.571 -4.073 2.245
## earlyacademicyear -2.245 1.575 -1.425 0.154 -5.332 0.842
## white 1.020 1.659 0.615 0.539 -2.231 4.271
## structuraletiology 0.326 1.919 0.170 0.865 -3.434 4.087
## priorepilepsy 0.207 1.738 0.119 0.905 -3.199 3.614
## status -6.799 2.173 -3.128 0.002 -11.059 -2.540
## ageyears 0.100 0.157 0.638 0.524 -0.208 0.409
## SEXnumeric 0.107 1.614 0.066 0.947 -3.056 3.270
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.283 0.122 26.983 0.000 26.652 20.998
## arm -0.081 0.080 -1.010 0.313 0.922 0.789
## TYPESTATUSnumeric -0.189 0.082 -2.306 0.021 0.828 0.706
## HOSPITALONSETnumeric -0.377 0.096 -3.934 0.000 0.686 0.569
## day -0.047 0.078 -0.597 0.550 0.954 0.818
## earlyacademicyear -0.108 0.079 -1.366 0.172 0.898 0.770
## white 0.048 0.083 0.583 0.560 1.049 0.892
## structuraletiology 0.019 0.094 0.201 0.840 1.019 0.847
## priorepilepsy 0.001 0.081 0.017 0.987 1.001 0.854
## status -0.381 0.136 -2.809 0.005 0.683 0.524
## ageyears 0.005 0.007 0.634 0.526 1.005 0.990
## SEXnumeric 0.009 0.079 0.111 0.911 1.009 0.864
## upper .95
## intercept 33.830
## arm 1.079
## TYPESTATUSnumeric 0.972
## HOSPITALONSETnumeric 0.828
## day 1.113
## earlyacademicyear 1.048
## white 1.235
## structuraletiology 1.226
## priorepilepsy 1.174
## status 0.891
## ageyears 1.020
## SEXnumeric 1.178
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.666 0.133 20.059 0.000 14.378 11.081
## arm 0.093 0.083 1.120 0.263 1.098 0.932
## TYPESTATUSnumeric 0.196 0.077 2.550 0.011 1.217 1.046
## HOSPITALONSETnumeric 0.345 0.079 4.368 0.000 1.412 1.210
## day 0.046 0.084 0.543 0.587 1.047 0.887
## earlyacademicyear 0.118 0.080 1.468 0.142 1.125 0.961
## white -0.055 0.084 -0.658 0.510 0.946 0.803
## structuraletiology -0.014 0.098 -0.146 0.884 0.986 0.813
## priorepilepsy -0.020 0.095 -0.212 0.832 0.980 0.813
## status 0.320 0.100 3.193 0.001 1.376 1.131
## ageyears -0.005 0.008 -0.640 0.522 0.995 0.978
## SEXnumeric -0.003 0.083 -0.038 0.970 0.997 0.847
## upper .95
## intercept 18.655
## arm 1.292
## TYPESTATUSnumeric 1.415
## HOSPITALONSETnumeric 1.649
## day 1.235
## earlyacademicyear 1.317
## white 1.116
## structuraletiology 1.195
## priorepilepsy 1.181
## status 1.675
## ageyears 1.011
## SEXnumeric 1.173
# First BZD later than 60 minutes
CrossTable(pSERG$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 266 | 62 |
## | 0.811 | 0.189 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 117 | 34 |
## | 0.775 | 0.225 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 149 | 28 |
## | 0.842 | 0.158 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore60min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore60min and pSERG$awareness
## p-value = 0.1568
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3559881 1.1699788
## sample estimates:
## odds ratio
## 0.6475457
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=60,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.926 -7.539 1.687 0.214
## RMST (arm=1)/(arm=0) 0.897 0.745 1.079 0.249
## RMTL (arm=1)/(arm=0) 1.092 0.956 1.247 0.196
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.449 3.699 9.314 0.000 27.199 41.698
## arm -2.926 2.354 -1.243 0.214 -7.539 1.687
## TYPESTATUSnumeric -6.759 2.222 -3.042 0.002 -11.114 -2.405
## HOSPITALONSETnumeric -9.712 2.351 -4.132 0.000 -14.319 -5.105
## day -1.580 2.373 -0.666 0.506 -6.231 3.071
## earlyacademicyear -3.481 2.289 -1.520 0.128 -7.968 1.006
## white 0.871 2.452 0.355 0.723 -3.936 5.677
## structuraletiology 0.748 2.769 0.270 0.787 -4.680 6.176
## priorepilepsy 1.558 2.542 0.613 0.540 -3.425 6.540
## status -10.876 2.899 -3.752 0.000 -16.558 -5.194
## ageyears 0.145 0.227 0.636 0.525 -0.301 0.590
## SEXnumeric -0.642 2.347 -0.274 0.784 -5.243 3.959
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.575 0.144 24.869 0.000 35.681 26.921
## arm -0.109 0.094 -1.153 0.249 0.897 0.745
## TYPESTATUSnumeric -0.277 0.098 -2.835 0.005 0.758 0.625
## HOSPITALONSETnumeric -0.420 0.113 -3.734 0.000 0.657 0.527
## day -0.065 0.093 -0.695 0.487 0.937 0.781
## earlyacademicyear -0.134 0.093 -1.442 0.149 0.874 0.728
## white 0.031 0.099 0.317 0.751 1.032 0.850
## structuraletiology 0.033 0.109 0.302 0.762 1.034 0.834
## priorepilepsy 0.048 0.095 0.501 0.616 1.049 0.870
## status -0.506 0.154 -3.280 0.001 0.603 0.446
## ageyears 0.005 0.009 0.603 0.547 1.005 0.988
## SEXnumeric -0.020 0.093 -0.212 0.832 0.980 0.817
## upper .95
## intercept 47.292
## arm 1.079
## TYPESTATUSnumeric 0.918
## HOSPITALONSETnumeric 0.819
## day 1.125
## earlyacademicyear 1.049
## white 1.253
## structuraletiology 1.281
## priorepilepsy 1.264
## status 0.816
## ageyears 1.022
## SEXnumeric 1.177
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.272 0.111 29.446 0.000 26.356 21.198
## arm 0.088 0.068 1.294 0.196 1.092 0.956
## TYPESTATUSnumeric 0.192 0.062 3.104 0.002 1.212 1.073
## HOSPITALONSETnumeric 0.268 0.064 4.178 0.000 1.308 1.153
## day 0.045 0.070 0.650 0.516 1.047 0.912
## earlyacademicyear 0.103 0.066 1.560 0.119 1.108 0.974
## white -0.028 0.070 -0.394 0.694 0.973 0.847
## structuraletiology -0.020 0.081 -0.253 0.800 0.980 0.837
## priorepilepsy -0.053 0.078 -0.676 0.499 0.949 0.815
## status 0.294 0.078 3.763 0.000 1.342 1.151
## ageyears -0.004 0.007 -0.655 0.512 0.996 0.982
## SEXnumeric 0.020 0.068 0.298 0.766 1.020 0.893
## upper .95
## intercept 32.768
## arm 1.247
## TYPESTATUSnumeric 1.368
## HOSPITALONSETnumeric 1.483
## day 1.200
## earlyacademicyear 1.261
## white 1.116
## structuraletiology 1.147
## priorepilepsy 1.105
## status 1.564
## ageyears 1.009
## SEXnumeric 1.166
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 97 | 231 |
## | 0.296 | 0.704 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 45 | 106 |
## | 0.298 | 0.702 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 52 | 125 |
## | 0.294 | 0.706 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore40min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore40min and pSERG$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6159083 1.6873059
## sample estimates:
## odds ratio
## 1.020443
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=40,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.765 -2.625 1.096 0.421
## RMST (arm=1)/(arm=0) 0.979 0.928 1.033 0.436
## RMTL (arm=1)/(arm=0) 1.199 0.824 1.745 0.343
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.887 1.702 20.503 0.000 31.552 38.222
## arm -0.765 0.949 -0.806 0.421 -2.625 1.096
## TYPESTATUSnumeric 0.059 0.995 0.059 0.953 -1.891 2.009
## HOSPITALONSETnumeric -6.520 1.200 -5.431 0.000 -8.872 -4.167
## day -1.409 0.983 -1.433 0.152 -3.336 0.518
## earlyacademicyear 0.928 0.936 0.992 0.321 -0.906 2.763
## white 1.989 1.031 1.929 0.054 -0.032 4.010
## structuraletiology -1.491 1.165 -1.280 0.201 -3.775 0.793
## priorepilepsy 1.739 0.994 1.750 0.080 -0.209 3.687
## status -0.730 1.188 -0.614 0.539 -3.059 1.600
## ageyears 0.161 0.099 1.630 0.103 -0.033 0.354
## SEXnumeric 1.019 1.022 0.997 0.319 -0.984 3.021
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.549 0.049 71.697 0.000 34.763 31.549
## arm -0.021 0.027 -0.780 0.436 0.979 0.928
## TYPESTATUSnumeric 0.003 0.028 0.102 0.919 1.003 0.949
## HOSPITALONSETnumeric -0.193 0.038 -5.057 0.000 0.825 0.765
## day -0.039 0.028 -1.401 0.161 0.961 0.910
## earlyacademicyear 0.026 0.027 0.972 0.331 1.026 0.974
## white 0.057 0.030 1.885 0.059 1.058 0.998
## structuraletiology -0.043 0.035 -1.238 0.216 0.958 0.895
## priorepilepsy 0.049 0.028 1.721 0.085 1.050 0.993
## status -0.020 0.034 -0.587 0.557 0.980 0.918
## ageyears 0.005 0.003 1.632 0.103 1.005 0.999
## SEXnumeric 0.028 0.029 0.965 0.335 1.029 0.971
## upper .95
## intercept 38.304
## arm 1.033
## TYPESTATUSnumeric 1.060
## HOSPITALONSETnumeric 0.889
## day 1.016
## earlyacademicyear 1.082
## white 1.123
## structuraletiology 1.025
## priorepilepsy 1.109
## status 1.047
## ageyears 1.010
## SEXnumeric 1.089
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.404 0.343 4.094 0.000 4.072 2.079
## arm 0.181 0.192 0.947 0.343 1.199 0.824
## TYPESTATUSnumeric 0.068 0.224 0.304 0.761 1.070 0.690
## HOSPITALONSETnumeric 1.230 0.213 5.760 0.000 3.420 2.251
## day 0.330 0.215 1.533 0.125 1.391 0.912
## earlyacademicyear -0.202 0.194 -1.044 0.296 0.817 0.559
## white -0.405 0.195 -2.075 0.038 0.667 0.455
## structuraletiology 0.286 0.199 1.438 0.150 1.331 0.901
## priorepilepsy -0.444 0.249 -1.786 0.074 0.641 0.394
## status 0.225 0.278 0.809 0.418 1.252 0.727
## ageyears -0.036 0.024 -1.515 0.130 0.964 0.920
## SEXnumeric -0.269 0.211 -1.277 0.202 0.764 0.506
## upper .95
## intercept 7.976
## arm 1.745
## TYPESTATUSnumeric 1.661
## HOSPITALONSETnumeric 5.197
## day 2.121
## earlyacademicyear 1.194
## white 0.978
## structuraletiology 1.965
## priorepilepsy 1.044
## status 2.157
## ageyears 1.011
## SEXnumeric 1.155
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 150 | 178 |
## | 0.457 | 0.543 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 70 | 81 |
## | 0.464 | 0.536 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 80 | 97 |
## | 0.452 | 0.548 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore60min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore60min and pSERG$awareness
## p-value = 0.9115
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6618423 1.6584742
## sample estimates:
## odds ratio
## 1.047657
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=60,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.616 -4.006 2.773 0.722
## RMST (arm=1)/(arm=0) 0.989 0.921 1.062 0.755
## RMTL (arm=1)/(arm=0) 1.076 0.815 1.419 0.605
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 48.523 3.037 15.975 0.000 42.570 54.477
## arm -0.616 1.729 -0.356 0.722 -4.006 2.773
## TYPESTATUSnumeric -2.347 1.831 -1.282 0.200 -5.936 1.241
## HOSPITALONSETnumeric -12.771 2.098 -6.088 0.000 -16.882 -8.659
## day -3.214 1.779 -1.807 0.071 -6.701 0.273
## earlyacademicyear 1.966 1.717 1.145 0.252 -1.399 5.331
## white 2.985 1.872 1.594 0.111 -0.685 6.656
## structuraletiology -3.222 2.079 -1.550 0.121 -7.297 0.853
## priorepilepsy 3.459 1.809 1.912 0.056 -0.087 7.006
## status -1.559 2.119 -0.736 0.462 -5.711 2.594
## ageyears 0.308 0.174 1.771 0.077 -0.033 0.649
## SEXnumeric 2.068 1.806 1.145 0.252 -1.472 5.608
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.876 0.065 59.317 0.000 48.242 42.443
## arm -0.011 0.036 -0.312 0.755 0.989 0.921
## TYPESTATUSnumeric -0.047 0.039 -1.219 0.223 0.954 0.884
## HOSPITALONSETnumeric -0.282 0.051 -5.514 0.000 0.754 0.683
## day -0.066 0.037 -1.781 0.075 0.936 0.870
## earlyacademicyear 0.040 0.036 1.107 0.268 1.041 0.970
## white 0.062 0.040 1.526 0.127 1.064 0.983
## structuraletiology -0.069 0.047 -1.473 0.141 0.934 0.852
## priorepilepsy 0.070 0.038 1.868 0.062 1.073 0.997
## status -0.030 0.044 -0.685 0.493 0.970 0.890
## ageyears 0.006 0.004 1.770 0.077 1.006 0.999
## SEXnumeric 0.042 0.038 1.089 0.276 1.042 0.967
## upper .95
## intercept 54.834
## arm 1.062
## TYPESTATUSnumeric 1.029
## HOSPITALONSETnumeric 0.834
## day 1.007
## earlyacademicyear 1.117
## white 1.152
## structuraletiology 1.023
## priorepilepsy 1.155
## status 1.058
## ageyears 1.013
## SEXnumeric 1.123
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.328 0.242 9.610 0.000 10.259 6.381
## arm 0.073 0.141 0.517 0.605 1.076 0.815
## TYPESTATUSnumeric 0.230 0.151 1.524 0.127 1.259 0.936
## HOSPITALONSETnumeric 0.968 0.150 6.468 0.000 2.632 1.963
## day 0.281 0.157 1.796 0.073 1.325 0.975
## earlyacademicyear -0.179 0.145 -1.234 0.217 0.837 0.630
## white -0.261 0.144 -1.822 0.068 0.770 0.581
## structuraletiology 0.255 0.146 1.741 0.082 1.290 0.968
## priorepilepsy -0.338 0.172 -1.963 0.050 0.713 0.509
## status 0.178 0.192 0.929 0.353 1.195 0.821
## ageyears -0.028 0.017 -1.684 0.092 0.972 0.941
## SEXnumeric -0.209 0.150 -1.401 0.161 0.811 0.605
## upper .95
## intercept 16.494
## arm 1.419
## TYPESTATUSnumeric 1.692
## HOSPITALONSETnumeric 3.529
## day 1.801
## earlyacademicyear 1.111
## white 1.020
## structuraletiology 1.718
## priorepilepsy 1.000
## status 1.741
## ageyears 1.005
## SEXnumeric 1.087
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 233 | 95 |
## | 0.710 | 0.290 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 151
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 105 | 46 |
## | 0.695 | 0.305 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 177
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 128 | 49 |
## | 0.723 | 0.277 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore120min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore120min and pSERG$awareness
## p-value = 0.6258
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5268943 1.4511690
## sample estimates:
## odds ratio
## 0.8741735
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=120,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.095 -10.081 5.891 0.607
## RMST (arm=1)/(arm=0) 0.977 0.875 1.091 0.681
## RMTL (arm=1)/(arm=0) 1.060 0.894 1.258 0.502
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 83.165 6.819 12.197 0.000 69.801 96.529
## arm -2.095 4.074 -0.514 0.607 -10.081 5.891
## TYPESTATUSnumeric -19.028 4.071 -4.674 0.000 -27.007 -11.049
## HOSPITALONSETnumeric -28.314 4.517 -6.269 0.000 -37.167 -19.462
## day -7.717 4.120 -1.873 0.061 -15.793 0.359
## earlyacademicyear 0.699 4.044 0.173 0.863 -7.227 8.625
## white 4.828 4.281 1.128 0.259 -3.563 13.218
## structuraletiology -8.691 4.813 -1.806 0.071 -18.124 0.742
## priorepilepsy 6.993 4.336 1.613 0.107 -1.505 15.492
## status -6.463 5.067 -1.275 0.202 -16.394 3.469
## ageyears 0.799 0.387 2.066 0.039 0.041 1.557
## SEXnumeric 3.560 4.178 0.852 0.394 -4.628 11.748
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.421 0.096 46.043 0.000 83.185 68.915
## arm -0.023 0.056 -0.411 0.681 0.977 0.875
## TYPESTATUSnumeric -0.273 0.062 -4.432 0.000 0.761 0.674
## HOSPITALONSETnumeric -0.423 0.076 -5.589 0.000 0.655 0.565
## day -0.106 0.057 -1.882 0.060 0.899 0.805
## earlyacademicyear 0.007 0.056 0.133 0.894 1.008 0.902
## white 0.060 0.061 0.977 0.329 1.062 0.941
## structuraletiology -0.122 0.074 -1.665 0.096 0.885 0.766
## priorepilepsy 0.090 0.058 1.549 0.121 1.095 0.976
## status -0.080 0.072 -1.123 0.262 0.923 0.802
## ageyears 0.010 0.005 2.021 0.043 1.010 1.000
## SEXnumeric 0.046 0.058 0.801 0.423 1.048 0.935
## upper .95
## intercept 100.411
## arm 1.091
## TYPESTATUSnumeric 0.859
## HOSPITALONSETnumeric 0.760
## day 1.004
## earlyacademicyear 1.125
## white 1.198
## structuraletiology 1.022
## priorepilepsy 1.227
## status 1.062
## ageyears 1.020
## SEXnumeric 1.174
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.607 0.145 24.800 0.000 36.848 27.709
## arm 0.059 0.087 0.672 0.502 1.060 0.894
## TYPESTATUSnumeric 0.390 0.083 4.672 0.000 1.476 1.254
## HOSPITALONSETnumeric 0.561 0.089 6.313 0.000 1.753 1.472
## day 0.164 0.092 1.787 0.074 1.178 0.984
## earlyacademicyear -0.019 0.087 -0.217 0.829 0.981 0.827
## white -0.119 0.087 -1.360 0.174 0.888 0.748
## structuraletiology 0.181 0.092 1.957 0.050 1.198 1.000
## priorepilepsy -0.168 0.101 -1.664 0.096 0.846 0.694
## status 0.161 0.110 1.462 0.144 1.174 0.947
## ageyears -0.019 0.009 -2.034 0.042 0.981 0.963
## SEXnumeric -0.086 0.090 -0.963 0.336 0.917 0.770
## upper .95
## intercept 49.001
## arm 1.258
## TYPESTATUSnumeric 1.739
## HOSPITALONSETnumeric 2.086
## day 1.410
## earlyacademicyear 1.164
## white 1.054
## structuraletiology 1.436
## priorepilepsy 1.030
## status 1.457
## ageyears 0.999
## SEXnumeric 1.094
# First CI later than 60 minutes
CrossTable(pSERG$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 152
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 17 | 135 |
## | 0.112 | 0.888 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 68
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 62 |
## | 0.088 | 0.912 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 11 | 73 |
## | 0.131 | 0.869 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstCImore60min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore60min and pSERG$awareness
## p-value = 0.4493
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1844316 2.0313749
## sample estimates:
## odds ratio
## 0.6440591
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.595 -2.407 3.596 0.698
## RMST (arm=1)/(arm=0) 1.010 0.959 1.065 0.695
## RMTL (arm=1)/(arm=0) 0.826 0.196 3.475 0.794
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 59.007 1.973 29.900 0.000 55.139 62.874
## arm 0.595 1.531 0.388 0.698 -2.407 3.596
## TYPESTATUSnumeric 0.056 1.614 0.034 0.972 -3.108 3.220
## HOSPITALONSETnumeric -1.228 1.737 -0.707 0.479 -4.632 2.175
## day -2.874 1.255 -2.290 0.022 -5.333 -0.414
## earlyacademicyear -0.573 1.449 -0.396 0.692 -3.413 2.266
## white 1.122 1.560 0.720 0.472 -1.935 4.180
## structuraletiology -0.846 1.868 -0.453 0.650 -4.508 2.815
## priorepilepsy -0.402 1.279 -0.314 0.753 -2.908 2.105
## status 2.962 1.121 2.643 0.008 0.766 5.159
## ageyears -0.009 0.147 -0.062 0.950 -0.297 0.279
## SEXnumeric -0.537 1.569 -0.343 0.732 -3.612 2.538
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.077 0.034 118.592 0.000 58.979 55.135
## arm 0.010 0.027 0.392 0.695 1.010 0.959
## TYPESTATUSnumeric 0.001 0.028 0.027 0.978 1.001 0.947
## HOSPITALONSETnumeric -0.021 0.031 -0.701 0.483 0.979 0.922
## day -0.050 0.022 -2.249 0.024 0.951 0.911
## earlyacademicyear -0.010 0.025 -0.399 0.690 0.990 0.942
## white 0.020 0.027 0.723 0.470 1.020 0.967
## structuraletiology -0.015 0.033 -0.444 0.657 0.985 0.923
## priorepilepsy -0.007 0.022 -0.311 0.756 0.993 0.951
## status 0.051 0.020 2.596 0.009 1.052 1.013
## ageyears 0.000 0.003 -0.057 0.954 1.000 0.995
## SEXnumeric -0.009 0.027 -0.345 0.730 0.991 0.939
## upper .95
## intercept 63.090
## arm 1.065
## TYPESTATUSnumeric 1.057
## HOSPITALONSETnumeric 1.039
## day 0.994
## earlyacademicyear 1.040
## white 1.077
## structuraletiology 1.052
## priorepilepsy 1.037
## status 1.093
## ageyears 1.005
## SEXnumeric 1.045
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept -0.444 1.087 -0.408 0.683 0.642 0.076
## arm -0.191 0.733 -0.261 0.794 0.826 0.196
## TYPESTATUSnumeric -0.190 0.780 -0.244 0.807 0.827 0.179
## HOSPITALONSETnumeric 0.517 0.632 0.818 0.414 1.676 0.486
## day 1.574 0.843 1.867 0.062 4.826 0.925
## earlyacademicyear 0.180 0.627 0.287 0.774 1.197 0.351
## white -0.190 0.611 -0.312 0.755 0.827 0.250
## structuraletiology 0.408 0.500 0.814 0.416 1.503 0.564
## priorepilepsy 0.197 0.563 0.349 0.727 1.217 0.403
## status -17.676 0.504 -35.052 0.000 0.000 0.000
## ageyears 0.013 0.054 0.239 0.811 1.013 0.911
## SEXnumeric 0.135 0.668 0.202 0.840 1.145 0.309
## upper .95
## intercept 5.398
## arm 3.475
## TYPESTATUSnumeric 3.816
## HOSPITALONSETnumeric 5.783
## day 25.179
## earlyacademicyear 4.086
## white 2.737
## structuraletiology 4.009
## priorepilepsy 3.673
## status 0.000
## ageyears 1.126
## SEXnumeric 4.241
# First CI later than 120 minutes
CrossTable(pSERG$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 152
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 45 | 107 |
## | 0.296 | 0.704 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 68
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 16 | 52 |
## | 0.235 | 0.765 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 29 | 55 |
## | 0.345 | 0.655 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstCImore120min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore120min and pSERG$awareness
## p-value = 0.1562
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2642823 1.2638205
## sample estimates:
## odds ratio
## 0.5856217
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -5.444 -14.181 3.293 0.222
## RMST (arm=1)/(arm=0) 0.950 0.874 1.032 0.221
## RMTL (arm=1)/(arm=0) 1.487 0.753 2.934 0.253
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 113.407 6.884 16.475 0.000 99.915 126.899
## arm -5.444 4.458 -1.221 0.222 -14.181 3.293
## TYPESTATUSnumeric -1.290 5.082 -0.254 0.800 -11.251 8.670
## HOSPITALONSETnumeric -0.920 4.877 -0.189 0.850 -10.478 8.638
## day -7.022 4.319 -1.626 0.104 -15.486 1.442
## earlyacademicyear -3.668 4.654 -0.788 0.431 -12.789 5.453
## white 3.105 4.830 0.643 0.520 -6.361 12.572
## structuraletiology -3.877 5.799 -0.669 0.504 -15.243 7.489
## priorepilepsy -4.649 4.734 -0.982 0.326 -13.928 4.630
## status 9.072 4.614 1.966 0.049 0.028 18.115
## ageyears 0.318 0.409 0.778 0.437 -0.484 1.121
## SEXnumeric -1.571 4.635 -0.339 0.735 -10.656 7.513
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.733 0.064 73.450 0.000 113.581 100.106
## arm -0.052 0.042 -1.224 0.221 0.950 0.874
## TYPESTATUSnumeric -0.012 0.049 -0.240 0.811 0.988 0.898
## HOSPITALONSETnumeric -0.009 0.046 -0.185 0.853 0.991 0.906
## day -0.066 0.041 -1.615 0.106 0.936 0.863
## earlyacademicyear -0.035 0.044 -0.803 0.422 0.965 0.886
## white 0.030 0.046 0.643 0.521 1.030 0.941
## structuraletiology -0.037 0.056 -0.665 0.506 0.963 0.863
## priorepilepsy -0.045 0.045 -0.984 0.325 0.956 0.875
## status 0.085 0.043 1.969 0.049 1.089 1.000
## ageyears 0.003 0.004 0.778 0.436 1.003 0.995
## SEXnumeric -0.015 0.044 -0.343 0.732 0.985 0.905
## upper .95
## intercept 128.869
## arm 1.032
## TYPESTATUSnumeric 1.087
## HOSPITALONSETnumeric 1.085
## day 1.014
## earlyacademicyear 1.052
## white 1.127
## structuraletiology 1.076
## priorepilepsy 1.045
## status 1.186
## ageyears 1.011
## SEXnumeric 1.073
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.050 0.541 3.790 0.000 7.767 2.691
## arm 0.397 0.347 1.144 0.253 1.487 0.753
## TYPESTATUSnumeric 0.119 0.333 0.358 0.720 1.127 0.587
## HOSPITALONSETnumeric 0.069 0.340 0.203 0.839 1.071 0.551
## day 0.508 0.323 1.571 0.116 1.662 0.882
## earlyacademicyear 0.228 0.336 0.680 0.496 1.257 0.651
## white -0.214 0.338 -0.634 0.526 0.807 0.416
## structuraletiology 0.240 0.358 0.671 0.502 1.271 0.631
## priorepilepsy 0.298 0.320 0.930 0.352 1.347 0.719
## status -0.704 0.420 -1.678 0.093 0.494 0.217
## ageyears -0.024 0.031 -0.763 0.445 0.977 0.919
## SEXnumeric 0.106 0.345 0.306 0.759 1.111 0.565
## upper .95
## intercept 22.419
## arm 2.934
## TYPESTATUSnumeric 2.164
## HOSPITALONSETnumeric 2.085
## day 3.130
## earlyacademicyear 2.427
## white 1.566
## structuraletiology 2.563
## priorepilepsy 2.525
## status 1.126
## ageyears 1.038
## SEXnumeric 2.184
# First CI later than 240 minutes
CrossTable(pSERG$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 152
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 93 | 59 |
## | 0.612 | 0.388 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 68
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 39 | 29 |
## | 0.574 | 0.426 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 54 | 30 |
## | 0.643 | 0.357 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstCImore240min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore240min and pSERG$awareness
## p-value = 0.4067
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3683156 1.5175244
## sample estimates:
## odds ratio
## 0.7485821
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -12.694 -35.823 10.436 0.282
## RMST (arm=1)/(arm=0) 0.928 0.809 1.065 0.288
## RMTL (arm=1)/(arm=0) 1.202 0.864 1.671 0.275
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 196.605 20.167 9.749 0.000 157.078 236.131
## arm -12.694 11.801 -1.076 0.282 -35.823 10.436
## TYPESTATUSnumeric -22.437 13.057 -1.718 0.086 -48.028 3.154
## HOSPITALONSETnumeric -5.896 12.593 -0.468 0.640 -30.577 18.786
## day -14.568 12.233 -1.191 0.234 -38.544 9.409
## earlyacademicyear -9.542 12.416 -0.769 0.442 -33.877 14.792
## white 5.947 13.065 0.455 0.649 -19.660 31.555
## structuraletiology -4.624 15.201 -0.304 0.761 -34.417 25.168
## priorepilepsy -5.716 13.797 -0.414 0.679 -32.758 21.325
## status 4.460 14.272 0.313 0.755 -23.512 32.432
## ageyears 0.348 1.099 0.317 0.751 -1.806 2.502
## SEXnumeric -7.172 12.097 -0.593 0.553 -30.881 16.537
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.291 0.118 45.000 0.000 198.538 157.674
## arm -0.075 0.070 -1.063 0.288 0.928 0.809
## TYPESTATUSnumeric -0.139 0.084 -1.660 0.097 0.870 0.738
## HOSPITALONSETnumeric -0.035 0.076 -0.459 0.646 0.966 0.832
## day -0.085 0.072 -1.177 0.239 0.918 0.797
## earlyacademicyear -0.056 0.074 -0.759 0.448 0.945 0.818
## white 0.035 0.078 0.442 0.659 1.035 0.888
## structuraletiology -0.030 0.092 -0.322 0.748 0.971 0.811
## priorepilepsy -0.034 0.082 -0.418 0.676 0.966 0.824
## status 0.029 0.085 0.343 0.732 1.030 0.872
## ageyears 0.002 0.006 0.296 0.767 1.002 0.989
## SEXnumeric -0.043 0.072 -0.598 0.550 0.958 0.833
## upper .95
## intercept 249.991
## arm 1.065
## TYPESTATUSnumeric 1.025
## HOSPITALONSETnumeric 1.121
## day 1.058
## earlyacademicyear 1.093
## white 1.207
## structuraletiology 1.163
## priorepilepsy 1.134
## status 1.216
## ageyears 1.015
## SEXnumeric 1.103
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.857 0.299 12.920 0.000 47.315 26.357
## arm 0.184 0.168 1.092 0.275 1.202 0.864
## TYPESTATUSnumeric 0.291 0.162 1.798 0.072 1.338 0.974
## HOSPITALONSETnumeric 0.084 0.171 0.490 0.624 1.088 0.777
## day 0.215 0.179 1.201 0.230 1.239 0.873
## earlyacademicyear 0.137 0.174 0.789 0.430 1.147 0.816
## white -0.088 0.181 -0.488 0.626 0.915 0.642
## structuraletiology 0.053 0.205 0.258 0.797 1.054 0.705
## priorepilepsy 0.078 0.195 0.402 0.687 1.082 0.738
## status -0.047 0.201 -0.235 0.814 0.954 0.643
## ageyears -0.006 0.016 -0.365 0.715 0.994 0.963
## SEXnumeric 0.099 0.172 0.579 0.563 1.105 0.789
## upper .95
## intercept 84.936
## arm 1.671
## TYPESTATUSnumeric 1.837
## HOSPITALONSETnumeric 1.522
## day 1.759
## earlyacademicyear 1.614
## white 1.306
## structuraletiology 1.576
## priorepilepsy 1.585
## status 1.414
## ageyears 1.026
## SEXnumeric 1.547
Time to treatment out of the hospital
# At least one benzodiazepine before hospital arrival
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 157
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 59 | 98 |
## | 0.376 | 0.624 |
## |-----------|-----------|
##
##
##
##
# Proportion of patients with at least one benzodiazepine before hospital arrival depending on awareness
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 81
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 40 | 41 |
## | 0.494 | 0.506 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 76
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 19 | 57 |
## | 0.250 | 0.750 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness)
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$AEDbeforehospital and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.001809
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.413809 6.132627
## sample estimates:
## odds ratio
## 2.906258
# Absolute risk reduction
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$AEDbeforehospital)$prop.row[2] - CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$AEDbeforehospital)$prop.row[2]
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 81
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 40 | 41 |
## | 0.494 | 0.506 |
## |-----------|-----------|
##
##
##
##
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 76
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 19 | 57 |
## | 0.250 | 0.750 |
## |-----------|-----------|
##
##
##
##
## [1] -0.2438272
# Number needed to treat
1 / (CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$AEDbeforehospital)$prop.row[2] - CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$AEDbeforehospital)$prop.row[2])
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 81
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 40 | 41 |
## | 0.494 | 0.506 |
## |-----------|-----------|
##
##
##
##
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 76
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 19 | 57 |
## | 0.250 | 0.750 |
## |-----------|-----------|
##
##
##
##
## [1] -4.101266
# By year
table(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$yearSE=="2011", ]$AEDbeforehospital)
## < table of extent 0 >
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$yearSE=="2012", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 33
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 18 | 15 |
## | 0.545 | 0.455 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$yearSE=="2013", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 12 | 11 |
## | 0.522 | 0.478 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$yearSE=="2014", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 25
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 10 | 15 |
## | 0.400 | 0.600 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$yearSE=="2015", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 11
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 2 | 9 |
## | 0.182 | 0.818 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$yearSE=="2016", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 34
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 25 |
## | 0.265 | 0.735 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$yearSE=="2017", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 13
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 2 | 11 |
## | 0.154 | 0.846 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$yearSE=="2018", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 10
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 5 | 5 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$yearSE=="2019", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 8
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 1 | 7 |
## | 0.125 | 0.875 |
## |-----------|-----------|
##
##
##
##
# Logistic regression adjusting for potential confounders
logistic_out_of_hospital_BZD <- glm(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX, family="binomial")
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD), confint(logistic_out_of_hospital_BZD, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD))[ , 4])
## Waiting for profiling to be done...
## Odds ratio
## (Intercept) 2.7185651
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 4.3452719
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.2857623
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.1601618
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.1101262
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.5400745
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.7278006
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.1424542
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 9.0351191
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0292742
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.5171305
## 2.5 %
## (Intercept) 0.7359824
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.9590088
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.1099761
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.5293899
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.5185347
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.2249018
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.2946216
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.5322064
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.5239374
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9585850
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.2315641
## 97.5 %
## (Intercept) 10.8009006
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 10.3000467
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6892508
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 2.5594053
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.3789177
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.2438644
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.7903300
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 2.4626295
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 45.3025920
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.1084247
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.1148369
## p-value
## (Intercept) 0.141589849
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.000482221
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.007050196
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.710347135
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.787108224
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.155615525
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.487176056
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.732117993
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.002226171
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.432496538
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.098247513
# At least one benzodiazepine before hospital arrival among those with prior epilepsy
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 85
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 29 | 56 |
## | 0.341 | 0.659 |
## |-----------|-----------|
##
##
##
##
# At least one benzodiazepine before hospital arrival among those with prior epilepsy depending on awareness
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness == 0), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 43
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 23 |
## | 0.465 | 0.535 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness == 1), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 42
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 33 |
## | 0.214 | 0.786 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$awareness)
##
## Fisher's Exact Test for Count Data
##
## data:
## p-value = 0.02174
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.127161 9.360579
## sample estimates:
## odds ratio
## 3.143665
# Absolute risk reduction
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness == 0), ]$AEDbeforehospital)$prop.row[2] - CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness == 1), ]$AEDbeforehospital)$prop.row[2]
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 43
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 23 |
## | 0.465 | 0.535 |
## |-----------|-----------|
##
##
##
##
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 42
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 33 |
## | 0.214 | 0.786 |
## |-----------|-----------|
##
##
##
##
## [1] -0.2508306
# Number needed to treat
1 / (CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness == 0), ]$AEDbeforehospital)$prop.row[2] - CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness == 1), ]$AEDbeforehospital)$prop.row[2])
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 43
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 23 |
## | 0.465 | 0.535 |
## |-----------|-----------|
##
##
##
##
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 42
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 33 |
## | 0.214 | 0.786 |
## |-----------|-----------|
##
##
##
##
## [1] -3.986755
# By year
table(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$yearSE=="2011", ]$AEDbeforehospital)
## < table of extent 0 >
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$yearSE=="2012", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 19
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 12 | 7 |
## | 0.632 | 0.368 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$yearSE=="2013", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 10
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 7 |
## | 0.300 | 0.700 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$yearSE=="2014", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 14
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 5 | 9 |
## | 0.357 | 0.643 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$yearSE=="2015", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 7
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 1 | 6 |
## | 0.143 | 0.857 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$yearSE=="2016", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 17
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 7 | 10 |
## | 0.412 | 0.588 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$yearSE=="2017", ]$AEDbeforehospital)
##
## 1
## 9
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$yearSE=="2018", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 4
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 1 | 3 |
## | 0.250 | 0.750 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$yearSE=="2019", ]$AEDbeforehospital)
##
## 1
## 5
# Logistic regression adjusting for potential confounders among those with prior epilepsy
logistic_out_of_hospital_BZD_prior_epilepsy <- glm(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital ~ pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$awareness + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$day + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$white +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$structuraletiology +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$status + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$SEX, family="binomial")
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD_prior_epilepsy), confint(logistic_out_of_hospital_BZD_prior_epilepsy, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD_prior_epilepsy))[ , 4])
## Waiting for profiling to be done...
## Odds ratio
## (Intercept) 1.3359758
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness 3.9696983
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.4414770
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 1.7217193
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.7114983
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.5448214
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 1.1357661
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 10.1632513
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 1.0726123
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.6937318
## 2.5 %
## (Intercept) 0.1835707
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness 1.2819824
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.1110689
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 0.5777862
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.2243768
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.1545398
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 0.3176501
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 2.1435310
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 0.9580311
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.2223616
## 97.5 %
## (Intercept) 10.695066
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness 13.986966
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 1.554206
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 5.297892
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 2.210763
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 1.763946
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 4.245993
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 79.492673
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 1.213215
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 2.072260
## p-value
## (Intercept) 0.777123655
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness 0.022041943
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.217326455
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 0.331929707
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.555858376
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.321953266
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 0.845473896
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 0.009179252
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 0.238047384
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.516393858
## Correction for multiple comparisons
atleastoneBZD <- c(fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness)$p.value,
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD), confint(logistic_out_of_hospital_BZD, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD))[ , 4])[2,4],
fisher.test(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$awareness)$p.value,
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD_prior_epilepsy), confint(logistic_out_of_hospital_BZD_prior_epilepsy, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD_prior_epilepsy))[ , 4])[2,4]
)
## Waiting for profiling to be done...
## Waiting for profiling to be done...
p.adjust(atleastoneBZD, "BH")
## [1] 0.003617363 0.001928884 0.022041943 0.022041943
# Patients in each category
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$awareness)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 105 | 117 |
## | 0.473 | 0.527 |
## |-----------|-----------|
##
##
##
##
# Time to first BZD
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 7.00 20.00 68.93 55.00 1264.00
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0)
## [1] 153.5504
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 222 222 20 20 30
# Figure time to first BZD
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 7.0 25.0 63.1 60.0 720.0
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 7.00 20.00 74.15 50.00 1264.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 105 52.8 57.6
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 117 63.5 58.7
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 0.406 1.27
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 0.399 1.27
##
## Chisq= 1.3 on 1 degrees of freedom, p= 0.3
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.2606017
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 222, number of events= 222
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.0572927
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.4315180
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.0620423
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.1334490
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.1075034
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.1740010
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.0577160
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.6101583
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.0008181
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.1517755
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.0589657
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6495224
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.0640073
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.1427630
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.1134946
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.1900568
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0594141
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.8407228
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9991823
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.1638990
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.1402286
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.1567867
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.1416562
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.1384930
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.1510865
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.1743550
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.1489168
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.1940220
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.0143055
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.1423701
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.409
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -2.752
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.438
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.964
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.712
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.998
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.388
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 3.145
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.057
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.066
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.68286
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.00592 **
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.66140
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.33526
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.47675
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.31829
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.69833
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.00166 **
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.95440
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.28639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.0590
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6495
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.0640
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.1428
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.1135
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.1901
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0594
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.8407
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9992
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.1639
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.9443
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.5396
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.9398
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8751
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.8981
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8403
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.9439
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.5433
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0008
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8592
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.8045
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.4777
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8061
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8711
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.8281
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8456
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.7912
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.2585
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9716
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8805
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.3939
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.8832
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.4045
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.4991
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.4972
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.6749
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.4185
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.6924
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0276
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.5385
##
## Concordance= 0.6 (se = 0.025 )
## Rsquare= 0.102 (max possible= 1 )
## Likelihood ratio test= 23.94 on 10 df, p=0.008
## Wald test = 25.59 on 10 df, p=0.004
## Score (logrank) test = 26.56 on 10 df, p=0.003
# Time to first non-BZD AED
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 45.5 81.0 192.7 170.0 4320.0
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0)
## [1] 375.2449
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 222 222 81 70 103
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 45.0 82.0 189.3 190.0 1800.0
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 55.0 80.0 195.8 153.0 4320.0
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 105 53.0 53.3
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 117 59.4 59.1
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 0.00171 0.00492
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 0.00154 0.00492
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.9441036
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 222, number of events= 222
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.021747
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.760795
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.149188
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.055396
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.090119
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology -0.003863
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy -0.130302
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.111409
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.021653
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.240906
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.021985
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.467295
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.160891
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.056959
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.913822
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.996144
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.877830
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.117852
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.978580
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.272401
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.140321
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.154158
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.145294
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.139822
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.146181
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.171752
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.152081
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.194062
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.013936
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.148227
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.155
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -4.935
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.027
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.396
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.616
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology -0.022
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy -0.857
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.574
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -1.554
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.625
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.877
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 8.01e-07 ***
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.305
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.692
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.538
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.982
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.392
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.566
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.120
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.0220
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.4673
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.1609
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.0570
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.9138
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.9961
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.8778
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.1179
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9786
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.2724
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.9785
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 2.1400
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8614
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.9461
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.0943
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.0039
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.1392
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.8946
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0219
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7859
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.7763
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.3454
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8732
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8036
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.6862
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.7114
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.6516
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.7642
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9522
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.9516
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.3455
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6321
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.5434
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.3902
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.2170
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.3948
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.1827
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.6352
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0057
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.7014
##
## Concordance= 0.621 (se = 0.023 )
## Rsquare= 0.153 (max possible= 1 )
## Likelihood ratio test= 36.81 on 10 df, p=6e-05
## Wald test = 37.37 on 10 df, p=5e-05
## Score (logrank) test = 39.04 on 10 df, p=3e-05
# Time to first CI
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 15.0 118.0 172.0 506.4 626.0 4320.0 121
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## 1)
##
## 121 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 101 101 172 150 295
# Figure time to first CI
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20.0 137.0 180.0 575.8 660.0 4320.0 60
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 15.0 86.5 166.0 450.6 586.5 3008.0 61
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness, rho = 1)
##
## n=101, 121 observations deleted due to missingness.
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 45 21.3 24.2
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 56 29.8 26.9
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 0.352 1
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 0.317 1
##
## Chisq= 1 on 1 degrees of freedom, p= 0.3
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.3164683
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 101, number of events= 101
## (121 observations deleted due to missingness)
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.242082
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.158051
## pSERG[pSERG$HOSPITALONSET == "no", ]$day -0.155402
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.355585
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.401119
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.429897
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.220390
## pSERG[pSERG$HOSPITALONSET == "no", ]$status -0.024234
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.002679
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.182839
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.273899
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.853806
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.856071
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.427015
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.669570
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.537099
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.246563
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.976057
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.002682
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.200621
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.223437
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.243565
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.220719
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.232539
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.263308
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.283451
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.252103
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.278004
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.021505
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.238978
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.083
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.649
## pSERG[pSERG$HOSPITALONSET == "no", ]$day -0.704
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.529
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -1.523
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.517
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.874
## pSERG[pSERG$HOSPITALONSET == "no", ]$status -0.087
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.125
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.765
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.279
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.516
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.481
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.126
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.128
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.129
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.382
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.931
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.901
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.444
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.2739
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.8538
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8561
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.4270
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.6696
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.5371
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.2466
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.9761
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0027
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.2006
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.7850
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.1712
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.1681
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.7008
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.4935
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.6506
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.8022
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.0245
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9973
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8329
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.8221
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.5297
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.5554
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.9047
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.3996
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8819
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.7605
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.5660
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9613
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7516
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.974
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.376
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.319
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.251
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.122
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 2.679
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 2.043
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.683
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.046
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.918
##
## Concordance= 0.574 (se = 0.034 )
## Rsquare= 0.083 (max possible= 0.999 )
## Likelihood ratio test= 8.8 on 10 df, p=0.6
## Wald test = 8.99 on 10 df, p=0.5
## Score (logrank) test = 8.97 on 10 df, p=0.5
#### Recommendations and outliers out of the hospital
# First BZD later than 20 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 115 | 107 |
## | 0.518 | 0.482 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 105
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 50 | 55 |
## | 0.476 | 0.524 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 117
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 65 | 52 |
## | 0.556 | 0.444 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore20min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore20min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.2821
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4140914 1.2767604
## sample estimates:
## odds ratio
## 0.7283348
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=20,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.558 -2.333 1.216 0.537
## RMST (arm=1)/(arm=0) 0.961 0.849 1.087 0.528
## RMTL (arm=1)/(arm=0) 1.102 0.791 1.536 0.566
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 18.160 1.554 11.683 0.000 15.114 21.207
## arm -0.558 0.905 -0.617 0.537 -2.333 1.216
## TYPESTATUSnumeric -0.069 0.924 -0.074 0.941 -1.879 1.742
## day -0.777 0.899 -0.864 0.388 -2.540 0.986
## earlyacademicyear -0.443 0.891 -0.497 0.619 -2.189 1.303
## white -0.585 0.935 -0.626 0.531 -2.418 1.248
## structuraletiology -0.996 1.091 -0.913 0.361 -3.134 1.142
## priorepilepsy -2.230 0.936 -2.384 0.017 -4.064 -0.396
## status -4.772 1.380 -3.459 0.001 -7.475 -2.068
## ageyears -0.034 0.093 -0.364 0.716 -0.216 0.149
## SEXnumeric 0.098 0.922 0.106 0.915 -1.709 1.905
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.920 0.104 28.127 0.000 18.546 15.132
## arm -0.040 0.063 -0.631 0.528 0.961 0.849
## TYPESTATUSnumeric -0.006 0.064 -0.092 0.927 0.994 0.878
## day -0.056 0.062 -0.894 0.371 0.946 0.837
## earlyacademicyear -0.030 0.061 -0.482 0.630 0.971 0.861
## white -0.039 0.064 -0.601 0.548 0.962 0.849
## structuraletiology -0.068 0.077 -0.887 0.375 0.934 0.804
## priorepilepsy -0.152 0.066 -2.319 0.020 0.859 0.755
## status -0.387 0.128 -3.012 0.003 0.679 0.528
## ageyears -0.003 0.006 -0.397 0.691 0.997 0.985
## SEXnumeric 0.009 0.064 0.133 0.894 1.009 0.889
## upper .95
## intercept 22.732
## arm 1.087
## TYPESTATUSnumeric 1.126
## day 1.069
## earlyacademicyear 1.095
## white 1.091
## structuraletiology 1.086
## priorepilepsy 0.977
## status 0.874
## ageyears 1.010
## SEXnumeric 1.144
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 0.974 0.328 2.967 0.003 2.649 1.392
## arm 0.097 0.169 0.575 0.566 1.102 0.791
## TYPESTATUSnumeric 0.007 0.171 0.038 0.969 1.007 0.720
## day 0.132 0.167 0.791 0.429 1.141 0.823
## earlyacademicyear 0.088 0.167 0.529 0.597 1.092 0.788
## white 0.117 0.176 0.664 0.507 1.124 0.796
## structuraletiology 0.186 0.192 0.970 0.332 1.204 0.827
## priorepilepsy 0.437 0.184 2.377 0.017 1.549 1.080
## status 0.666 0.178 3.737 0.000 1.947 1.373
## ageyears 0.005 0.017 0.272 0.785 1.005 0.972
## SEXnumeric -0.006 0.170 -0.033 0.973 0.994 0.712
## upper .95
## intercept 5.041
## arm 1.536
## TYPESTATUSnumeric 1.408
## day 1.581
## earlyacademicyear 1.514
## white 1.589
## structuraletiology 1.753
## priorepilepsy 2.222
## status 2.762
## ageyears 1.039
## SEXnumeric 1.388
# First BZD later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 151 | 71 |
## | 0.680 | 0.320 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 105
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 68 | 37 |
## | 0.648 | 0.352 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 117
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 83 | 34 |
## | 0.709 | 0.291 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore40min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore40min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.3874
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4111257 1.3780614
## sample estimates:
## odds ratio
## 0.7538266
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.931 -5.837 1.974 0.332
## RMST (arm=1)/(arm=0) 0.917 0.770 1.092 0.332
## RMTL (arm=1)/(arm=0) 1.118 0.891 1.402 0.335
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 28.476 3.150 9.040 0.000 22.302 34.649
## arm -1.931 1.993 -0.969 0.332 -5.837 1.974
## TYPESTATUSnumeric -3.187 1.951 -1.634 0.102 -7.011 0.636
## day -1.070 1.970 -0.543 0.587 -4.932 2.791
## earlyacademicyear -1.331 1.926 -0.691 0.489 -5.106 2.444
## white 0.317 2.040 0.156 0.876 -3.680 4.315
## structuraletiology 0.203 2.394 0.085 0.933 -4.490 4.895
## priorepilepsy -1.480 2.021 -0.732 0.464 -5.442 2.481
## status -8.965 2.745 -3.266 0.001 -14.344 -3.586
## ageyears -0.070 0.192 -0.365 0.715 -0.447 0.307
## SEXnumeric -0.144 2.007 -0.072 0.943 -4.078 3.789
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.367 0.135 24.983 0.000 29.004 22.270
## arm -0.086 0.089 -0.970 0.332 0.917 0.770
## TYPESTATUSnumeric -0.145 0.090 -1.614 0.107 0.865 0.726
## day -0.050 0.087 -0.579 0.562 0.951 0.802
## earlyacademicyear -0.055 0.085 -0.641 0.521 0.947 0.801
## white 0.018 0.091 0.200 0.841 1.018 0.852
## structuraletiology 0.012 0.104 0.120 0.905 1.012 0.826
## priorepilepsy -0.069 0.088 -0.778 0.437 0.934 0.785
## status -0.485 0.173 -2.798 0.005 0.616 0.438
## ageyears -0.003 0.008 -0.377 0.706 0.997 0.980
## SEXnumeric 0.000 0.089 -0.002 0.998 1.000 0.840
## upper .95
## intercept 37.774
## arm 1.092
## TYPESTATUSnumeric 1.032
## day 1.127
## earlyacademicyear 1.119
## white 1.218
## structuraletiology 1.240
## priorepilepsy 1.110
## status 0.865
## ageyears 1.014
## SEXnumeric 1.191
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.499 0.195 12.813 0.000 12.167 8.302
## arm 0.111 0.116 0.965 0.335 1.118 0.891
## TYPESTATUSnumeric 0.180 0.110 1.630 0.103 1.197 0.964
## day 0.058 0.116 0.502 0.616 1.060 0.845
## earlyacademicyear 0.083 0.112 0.743 0.457 1.087 0.873
## white -0.012 0.116 -0.104 0.917 0.988 0.786
## structuraletiology -0.007 0.142 -0.046 0.963 0.993 0.752
## priorepilepsy 0.081 0.120 0.676 0.499 1.085 0.857
## status 0.434 0.127 3.407 0.001 1.544 1.202
## ageyears 0.004 0.011 0.342 0.733 1.004 0.982
## SEXnumeric 0.018 0.117 0.150 0.881 1.018 0.809
## upper .95
## intercept 17.831
## arm 1.402
## TYPESTATUSnumeric 1.487
## day 1.329
## earlyacademicyear 1.353
## white 1.241
## structuraletiology 1.313
## priorepilepsy 1.373
## status 1.982
## ageyears 1.026
## SEXnumeric 1.280
# First BZD later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 175 | 47 |
## | 0.788 | 0.212 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 105
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 79 | 26 |
## | 0.752 | 0.248 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 117
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 96 | 21 |
## | 0.821 | 0.179 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.2505
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3289345 1.3348546
## sample estimates:
## odds ratio
## 0.6659073
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.822 -8.588 2.945 0.338
## RMST (arm=1)/(arm=0) 0.905 0.735 1.114 0.344
## RMTL (arm=1)/(arm=0) 1.094 0.912 1.312 0.333
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 36.710 4.676 7.850 0.000 27.545 45.876
## arm -2.822 2.942 -0.959 0.338 -8.588 2.945
## TYPESTATUSnumeric -6.183 2.845 -2.174 0.030 -11.759 -0.608
## day -1.991 2.944 -0.676 0.499 -7.762 3.779
## earlyacademicyear -2.187 2.861 -0.764 0.445 -7.794 3.421
## white 0.047 3.038 0.016 0.988 -5.907 6.002
## structuraletiology 0.723 3.534 0.204 0.838 -6.204 7.649
## priorepilepsy 0.639 3.001 0.213 0.831 -5.242 6.520
## status -13.660 3.673 -3.720 0.000 -20.858 -6.462
## ageyears -0.078 0.279 -0.280 0.780 -0.625 0.469
## SEXnumeric -0.920 2.972 -0.310 0.757 -6.745 4.905
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.624 0.160 22.624 0.000 37.486 27.386
## arm -0.100 0.106 -0.946 0.344 0.905 0.735
## TYPESTATUSnumeric -0.228 0.108 -2.107 0.035 0.796 0.644
## day -0.072 0.104 -0.695 0.487 0.931 0.759
## earlyacademicyear -0.071 0.102 -0.693 0.488 0.932 0.763
## white 0.007 0.110 0.063 0.949 1.007 0.812
## structuraletiology 0.030 0.121 0.249 0.804 1.031 0.812
## priorepilepsy 0.015 0.104 0.148 0.882 1.016 0.829
## status -0.610 0.196 -3.104 0.002 0.543 0.370
## ageyears -0.003 0.010 -0.300 0.764 0.997 0.978
## SEXnumeric -0.023 0.105 -0.218 0.828 0.977 0.795
## upper .95
## intercept 51.312
## arm 1.114
## TYPESTATUSnumeric 0.984
## day 1.140
## earlyacademicyear 1.138
## white 1.249
## structuraletiology 1.308
## priorepilepsy 1.245
## status 0.799
## ageyears 1.016
## SEXnumeric 1.202
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.177 0.156 20.364 0.000 23.967 17.654
## arm 0.090 0.093 0.967 0.333 1.094 0.912
## TYPESTATUSnumeric 0.191 0.088 2.174 0.030 1.211 1.019
## day 0.063 0.095 0.662 0.508 1.065 0.884
## earlyacademicyear 0.074 0.090 0.817 0.414 1.077 0.902
## white 0.002 0.095 0.019 0.985 1.002 0.832
## structuraletiology -0.020 0.115 -0.173 0.863 0.980 0.782
## priorepilepsy -0.025 0.097 -0.259 0.795 0.975 0.806
## status 0.378 0.100 3.798 0.000 1.460 1.201
## ageyears 0.002 0.009 0.255 0.799 1.002 0.985
## SEXnumeric 0.036 0.095 0.378 0.705 1.037 0.861
## upper .95
## intercept 32.539
## arm 1.312
## TYPESTATUSnumeric 1.438
## day 1.281
## earlyacademicyear 1.285
## white 1.207
## structuraletiology 1.228
## priorepilepsy 1.180
## status 1.775
## ageyears 1.020
## SEXnumeric 1.248
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 44 | 178 |
## | 0.198 | 0.802 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 105
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 23 | 82 |
## | 0.219 | 0.781 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 117
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 21 | 96 |
## | 0.179 | 0.821 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore40min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore40min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.5025
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6273175 2.6272827
## sample estimates:
## odds ratio
## 1.28078
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.249 -2.139 1.642 0.797
## RMST (arm=1)/(arm=0) 0.994 0.944 1.045 0.803
## RMTL (arm=1)/(arm=0) 1.120 0.561 2.236 0.747
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.841 1.848 18.850 0.000 31.219 38.464
## arm -0.249 0.965 -0.258 0.797 -2.139 1.642
## TYPESTATUSnumeric -1.285 0.937 -1.371 0.170 -3.122 0.552
## day -0.404 0.983 -0.410 0.681 -2.331 1.524
## earlyacademicyear 1.235 0.911 1.356 0.175 -0.550 3.021
## white 1.104 1.045 1.056 0.291 -0.944 3.152
## structuraletiology -0.203 1.300 -0.156 0.876 -2.751 2.345
## priorepilepsy 1.922 1.035 1.856 0.063 -0.108 3.951
## status 0.943 1.033 0.913 0.361 -1.081 2.968
## ageyears 0.023 0.101 0.223 0.823 -0.176 0.221
## SEXnumeric 1.312 1.046 1.253 0.210 -0.740 3.363
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.552 0.051 69.850 0.000 34.879 31.570
## arm -0.006 0.026 -0.250 0.803 0.994 0.944
## TYPESTATUSnumeric -0.035 0.026 -1.356 0.175 0.966 0.919
## day -0.011 0.026 -0.412 0.680 0.989 0.939
## earlyacademicyear 0.033 0.025 1.342 0.180 1.034 0.985
## white 0.030 0.028 1.048 0.294 1.030 0.974
## structuraletiology -0.005 0.035 -0.151 0.880 0.995 0.928
## priorepilepsy 0.052 0.028 1.846 0.065 1.053 0.997
## status 0.025 0.027 0.906 0.365 1.025 0.972
## ageyears 0.001 0.003 0.228 0.820 1.001 0.995
## SEXnumeric 0.035 0.028 1.235 0.217 1.036 0.980
## upper .95
## intercept 38.534
## arm 1.045
## TYPESTATUSnumeric 1.016
## day 1.042
## earlyacademicyear 1.085
## white 1.089
## structuraletiology 1.066
## priorepilepsy 1.112
## status 1.081
## ageyears 1.006
## SEXnumeric 1.095
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.731 0.531 3.259 0.001 5.648 1.994
## arm 0.114 0.353 0.322 0.747 1.120 0.561
## TYPESTATUSnumeric 0.459 0.328 1.400 0.161 1.583 0.832
## day 0.142 0.375 0.380 0.704 1.153 0.553
## earlyacademicyear -0.514 0.359 -1.431 0.152 0.598 0.296
## white -0.382 0.346 -1.102 0.270 0.683 0.346
## structuraletiology 0.083 0.409 0.202 0.840 1.086 0.487
## priorepilepsy -0.735 0.437 -1.682 0.093 0.480 0.204
## status -0.486 0.565 -0.859 0.390 0.615 0.203
## ageyears -0.006 0.041 -0.155 0.877 0.994 0.918
## SEXnumeric -0.527 0.366 -1.438 0.150 0.590 0.288
## upper .95
## intercept 15.998
## arm 2.236
## TYPESTATUSnumeric 3.010
## day 2.404
## earlyacademicyear 1.209
## white 1.346
## structuraletiology 2.420
## priorepilepsy 1.129
## status 1.863
## ageyears 1.076
## SEXnumeric 1.211
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 80 | 142 |
## | 0.360 | 0.640 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 105
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 40 | 65 |
## | 0.381 | 0.619 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 117
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 40 | 77 |
## | 0.342 | 0.658 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.5773
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6593377 2.1274157
## sample estimates:
## odds ratio
## 1.183705
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.901 -2.692 4.493 0.623
## RMST (arm=1)/(arm=0) 1.018 0.950 1.091 0.618
## RMTL (arm=1)/(arm=0) 0.901 0.576 1.408 0.647
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 47.246 3.408 13.863 0.000 40.566 53.926
## arm 0.901 1.833 0.491 0.623 -2.692 4.493
## TYPESTATUSnumeric -4.472 1.891 -2.365 0.018 -8.179 -0.766
## day -1.233 1.868 -0.660 0.509 -4.894 2.428
## earlyacademicyear 2.059 1.802 1.142 0.253 -1.473 5.591
## white 1.710 2.009 0.851 0.395 -2.227 5.648
## structuraletiology -0.394 2.453 -0.161 0.872 -5.201 4.414
## priorepilepsy 4.785 1.941 2.466 0.014 0.981 8.588
## status 1.735 2.045 0.848 0.396 -2.273 5.742
## ageyears 0.110 0.187 0.588 0.557 -0.257 0.478
## SEXnumeric 2.158 1.934 1.116 0.265 -1.632 5.948
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.856 0.068 56.645 0.000 47.296 41.388
## arm 0.018 0.035 0.498 0.618 1.018 0.950
## TYPESTATUSnumeric -0.087 0.038 -2.304 0.021 0.917 0.851
## day -0.024 0.036 -0.668 0.504 0.976 0.910
## earlyacademicyear 0.040 0.035 1.139 0.255 1.040 0.972
## white 0.033 0.040 0.844 0.399 1.034 0.957
## structuraletiology -0.007 0.048 -0.143 0.887 0.993 0.904
## priorepilepsy 0.092 0.038 2.446 0.014 1.097 1.019
## status 0.033 0.038 0.848 0.396 1.033 0.958
## ageyears 0.002 0.004 0.598 0.550 1.002 0.995
## SEXnumeric 0.041 0.038 1.095 0.274 1.042 0.968
## upper .95
## intercept 54.047
## arm 1.091
## TYPESTATUSnumeric 0.987
## day 1.048
## earlyacademicyear 1.114
## white 1.117
## structuraletiology 1.091
## priorepilepsy 1.181
## status 1.114
## ageyears 1.009
## SEXnumeric 1.122
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.557 0.364 7.031 0.000 12.902 6.325
## arm -0.105 0.228 -0.459 0.647 0.901 0.576
## TYPESTATUSnumeric 0.532 0.216 2.467 0.014 1.703 1.116
## day 0.144 0.241 0.600 0.549 1.155 0.721
## earlyacademicyear -0.264 0.239 -1.108 0.268 0.768 0.481
## white -0.202 0.229 -0.881 0.378 0.817 0.522
## structuraletiology 0.072 0.274 0.262 0.793 1.074 0.628
## priorepilepsy -0.616 0.271 -2.270 0.023 0.540 0.317
## status -0.245 0.329 -0.746 0.456 0.782 0.411
## ageyears -0.014 0.027 -0.500 0.617 0.987 0.936
## SEXnumeric -0.287 0.235 -1.220 0.222 0.751 0.474
## upper .95
## intercept 26.317
## arm 1.408
## TYPESTATUSnumeric 2.599
## day 1.853
## earlyacademicyear 1.226
## white 1.280
## structuraletiology 1.836
## priorepilepsy 0.919
## status 1.491
## ageyears 1.040
## SEXnumeric 1.190
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 144 | 78 |
## | 0.649 | 0.351 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 105
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 66 | 39 |
## | 0.629 | 0.371 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 117
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 78 | 39 |
## | 0.667 | 0.333 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore120min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore120min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.5757
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4695163 1.5256575
## sample estimates:
## odds ratio
## 0.8467956
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=120,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.271 -9.282 9.823 0.956
## RMST (arm=1)/(arm=0) 1.003 0.891 1.129 0.963
## RMTL (arm=1)/(arm=0) 0.991 0.774 1.268 0.941
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 82.357 8.027 10.260 0.000 66.624 98.091
## arm 0.271 4.874 0.055 0.956 -9.282 9.823
## TYPESTATUSnumeric -23.433 4.896 -4.786 0.000 -33.030 -13.837
## day -4.770 4.778 -0.998 0.318 -14.134 4.594
## earlyacademicyear 1.120 4.802 0.233 0.816 -8.292 10.533
## white 2.790 5.184 0.538 0.590 -7.370 12.950
## structuraletiology -3.895 6.295 -0.619 0.536 -16.233 8.443
## priorepilepsy 9.638 4.981 1.935 0.053 -0.123 19.400
## status -1.904 5.915 -0.322 0.748 -13.498 9.690
## ageyears 0.468 0.459 1.020 0.308 -0.432 1.368
## SEXnumeric 2.109 4.999 0.422 0.673 -7.689 11.907
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.401 0.101 43.587 0.000 81.501 66.869
## arm 0.003 0.060 0.047 0.963 1.003 0.891
## TYPESTATUSnumeric -0.305 0.068 -4.506 0.000 0.737 0.645
## day -0.057 0.059 -0.977 0.329 0.944 0.842
## earlyacademicyear 0.017 0.059 0.286 0.775 1.017 0.906
## white 0.034 0.066 0.519 0.604 1.035 0.909
## structuraletiology -0.046 0.081 -0.572 0.567 0.955 0.815
## priorepilepsy 0.118 0.062 1.914 0.056 1.125 0.997
## status -0.020 0.073 -0.277 0.782 0.980 0.849
## ageyears 0.005 0.005 1.025 0.305 1.006 0.995
## SEXnumeric 0.027 0.062 0.435 0.663 1.027 0.910
## upper .95
## intercept 99.335
## arm 1.129
## TYPESTATUSnumeric 0.842
## day 1.059
## earlyacademicyear 1.142
## white 1.178
## structuraletiology 1.119
## priorepilepsy 1.269
## status 1.131
## ageyears 1.016
## SEXnumeric 1.159
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.577 0.210 17.010 0.000 35.753 23.677
## arm -0.009 0.126 -0.075 0.941 0.991 0.774
## TYPESTATUSnumeric 0.568 0.122 4.660 0.000 1.765 1.390
## day 0.131 0.128 1.020 0.308 1.140 0.887
## earlyacademicyear -0.013 0.128 -0.101 0.919 0.987 0.769
## white -0.074 0.128 -0.576 0.565 0.929 0.723
## structuraletiology 0.108 0.152 0.712 0.476 1.114 0.827
## priorepilepsy -0.258 0.135 -1.909 0.056 0.773 0.593
## status 0.068 0.155 0.439 0.661 1.070 0.790
## ageyears -0.014 0.014 -0.989 0.323 0.986 0.960
## SEXnumeric -0.049 0.131 -0.376 0.707 0.952 0.736
## upper .95
## intercept 53.987
## arm 1.268
## TYPESTATUSnumeric 2.242
## day 1.464
## earlyacademicyear 1.268
## white 1.193
## structuraletiology 1.501
## priorepilepsy 1.007
## status 1.451
## ageyears 1.014
## SEXnumeric 1.231
# First CI later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 101
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 11 | 90 |
## | 0.109 | 0.891 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 45
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 42 |
## | 0.067 | 0.933 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 56
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 8 | 48 |
## | 0.143 | 0.857 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.3373
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.06935858 1.94926376
## sample estimates:
## odds ratio
## 0.43196
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.404 -2.722 1.914 0.733
## RMST (arm=1)/(arm=0) 0.993 0.954 1.033 0.724
## RMTL (arm=1)/(arm=0) 1.006 0.283 3.573 0.992
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 61.809 1.976 31.281 0.000 57.936 65.682
## arm -0.404 1.183 -0.342 0.733 -2.722 1.914
## TYPESTATUSnumeric -1.338 1.696 -0.789 0.430 -4.661 1.986
## day -1.722 1.291 -1.334 0.182 -4.253 0.808
## earlyacademicyear -1.544 1.255 -1.230 0.219 -4.004 0.917
## white 0.326 1.547 0.211 0.833 -2.706 3.357
## structuraletiology -1.007 1.844 -0.546 0.585 -4.622 2.607
## priorepilepsy -1.220 1.419 -0.860 0.390 -4.001 1.561
## status 3.073 1.332 2.308 0.021 0.463 5.683
## ageyears -0.182 0.158 -1.151 0.250 -0.492 0.128
## SEXnumeric -0.414 1.800 -0.230 0.818 -3.941 3.113
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.126 0.034 120.050 0.000 61.902 57.870
## arm -0.007 0.020 -0.354 0.724 0.993 0.954
## TYPESTATUSnumeric -0.023 0.030 -0.770 0.442 0.977 0.922
## day -0.030 0.022 -1.323 0.186 0.971 0.929
## earlyacademicyear -0.027 0.022 -1.224 0.221 0.974 0.933
## white 0.006 0.027 0.218 0.827 1.006 0.955
## structuraletiology -0.018 0.032 -0.541 0.589 0.983 0.922
## priorepilepsy -0.021 0.025 -0.848 0.396 0.979 0.933
## status 0.052 0.023 2.267 0.023 1.054 1.007
## ageyears -0.003 0.003 -1.126 0.260 0.997 0.991
## SEXnumeric -0.007 0.031 -0.232 0.817 0.993 0.934
## upper .95
## intercept 66.215
## arm 1.033
## TYPESTATUSnumeric 1.036
## day 1.014
## earlyacademicyear 1.016
## white 1.060
## structuraletiology 1.047
## priorepilepsy 1.028
## status 1.102
## ageyears 1.002
## SEXnumeric 1.055
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept -1.838 1.077 -1.706 0.088 0.159 0.019
## arm 0.006 0.646 0.010 0.992 1.006 0.283
## TYPESTATUSnumeric 0.779 0.718 1.084 0.278 2.179 0.533
## day 1.033 0.789 1.310 0.190 2.810 0.599
## earlyacademicyear 0.890 0.772 1.153 0.249 2.436 0.536
## white 0.076 0.907 0.083 0.934 1.079 0.182
## structuraletiology 0.455 0.696 0.654 0.513 1.577 0.403
## priorepilepsy 0.642 0.610 1.052 0.293 1.899 0.574
## status -17.597 0.636 -27.682 0.000 0.000 0.000
## ageyears 0.101 0.047 2.150 0.032 1.107 1.009
## SEXnumeric 0.096 0.805 0.120 0.905 1.101 0.227
## upper .95
## intercept 1.314
## arm 3.573
## TYPESTATUSnumeric 8.907
## day 13.180
## earlyacademicyear 11.067
## white 6.384
## structuraletiology 6.174
## priorepilepsy 6.280
## status 0.000
## ageyears 1.214
## SEXnumeric 5.338
# First CI later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 101
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 73 |
## | 0.277 | 0.723 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 45
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 10 | 35 |
## | 0.222 | 0.778 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 56
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 18 | 38 |
## | 0.321 | 0.679 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore120min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore120min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.3714
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2181885 1.6086080
## sample estimates:
## odds ratio
## 0.6061825
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -5.383 -14.817 4.052 0.263
## RMST (arm=1)/(arm=0) 0.950 0.868 1.039 0.259
## RMTL (arm=1)/(arm=0) 1.430 0.663 3.082 0.362
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 119.675 9.301 12.867 0.000 101.445 137.905
## arm -5.383 4.814 -1.118 0.263 -14.817 4.052
## TYPESTATUSnumeric -7.373 6.007 -1.227 0.220 -19.147 4.402
## day -7.316 5.153 -1.420 0.156 -17.415 2.784
## earlyacademicyear -7.570 5.321 -1.423 0.155 -17.999 2.860
## white 4.501 5.835 0.771 0.441 -6.936 15.937
## structuraletiology -3.727 7.082 -0.526 0.599 -17.608 10.153
## priorepilepsy -5.954 5.466 -1.089 0.276 -16.668 4.759
## status 11.562 5.269 2.194 0.028 1.235 21.889
## ageyears -0.098 0.491 -0.200 0.841 -1.061 0.864
## SEXnumeric -2.441 5.576 -0.438 0.662 -13.369 8.488
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.791 0.087 55.309 0.000 120.405 101.605
## arm -0.052 0.046 -1.130 0.259 0.950 0.868
## TYPESTATUSnumeric -0.069 0.059 -1.178 0.239 0.933 0.831
## day -0.068 0.048 -1.405 0.160 0.934 0.850
## earlyacademicyear -0.072 0.050 -1.426 0.154 0.931 0.843
## white 0.042 0.055 0.764 0.445 1.043 0.936
## structuraletiology -0.037 0.068 -0.547 0.584 0.963 0.843
## priorepilepsy -0.055 0.052 -1.068 0.286 0.946 0.854
## status 0.108 0.049 2.193 0.028 1.114 1.012
## ageyears -0.001 0.005 -0.202 0.840 0.999 0.990
## SEXnumeric -0.024 0.052 -0.450 0.653 0.977 0.881
## upper .95
## intercept 142.684
## arm 1.039
## TYPESTATUSnumeric 1.047
## day 1.027
## earlyacademicyear 1.027
## white 1.162
## structuraletiology 1.101
## priorepilepsy 1.047
## status 1.228
## ageyears 1.008
## SEXnumeric 1.082
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.546 0.797 1.939 0.052 4.691 0.984
## arm 0.357 0.392 0.912 0.362 1.430 0.663
## TYPESTATUSnumeric 0.550 0.369 1.489 0.137 1.733 0.840
## day 0.604 0.434 1.389 0.165 1.829 0.780
## earlyacademicyear 0.542 0.429 1.263 0.207 1.719 0.742
## white -0.364 0.474 -0.768 0.442 0.695 0.274
## structuraletiology 0.175 0.504 0.347 0.729 1.191 0.444
## priorepilepsy 0.451 0.383 1.179 0.239 1.570 0.742
## status -0.903 0.530 -1.706 0.088 0.405 0.144
## ageyears 0.005 0.037 0.134 0.893 1.005 0.935
## SEXnumeric 0.137 0.444 0.309 0.758 1.147 0.481
## upper .95
## intercept 22.368
## arm 3.082
## TYPESTATUSnumeric 3.573
## day 4.285
## earlyacademicyear 3.984
## white 1.760
## structuraletiology 3.196
## priorepilepsy 3.323
## status 1.144
## ageyears 1.080
## SEXnumeric 2.737
# First CI later than 240 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 101
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 60 | 41 |
## | 0.594 | 0.406 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 45
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 26 | 19 |
## | 0.578 | 0.422 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 56
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 34 | 22 |
## | 0.607 | 0.393 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore240min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore240min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.8395
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3699671 2.1261956
## sample estimates:
## odds ratio
## 0.8865199
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -6.028 -32.811 20.755 0.659
## RMST (arm=1)/(arm=0) 0.961 0.820 1.126 0.623
## RMTL (arm=1)/(arm=0) 1.068 0.722 1.581 0.742
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 205.103 25.955 7.902 0.000 154.231 255.974
## arm -6.028 13.665 -0.441 0.659 -32.811 20.755
## TYPESTATUSnumeric -35.976 16.276 -2.210 0.027 -67.876 -4.075
## day -13.064 15.116 -0.864 0.387 -42.691 16.564
## earlyacademicyear -23.262 14.402 -1.615 0.106 -51.490 4.967
## white 14.344 16.072 0.892 0.372 -17.157 45.844
## structuraletiology -11.834 18.869 -0.627 0.531 -48.816 25.148
## priorepilepsy 2.951 15.755 0.187 0.851 -27.927 33.830
## status 15.578 15.887 0.981 0.327 -15.559 46.715
## ageyears -1.133 1.272 -0.890 0.373 -3.626 1.361
## SEXnumeric -11.903 14.009 -0.850 0.395 -39.361 15.554
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.334 0.149 35.915 0.000 207.355 154.983
## arm -0.040 0.081 -0.491 0.623 0.961 0.820
## TYPESTATUSnumeric -0.223 0.106 -2.112 0.035 0.800 0.650
## day -0.071 0.087 -0.815 0.415 0.931 0.785
## earlyacademicyear -0.136 0.085 -1.605 0.109 0.873 0.739
## white 0.086 0.097 0.887 0.375 1.090 0.901
## structuraletiology -0.076 0.114 -0.666 0.505 0.927 0.742
## priorepilepsy 0.020 0.091 0.218 0.827 1.020 0.853
## status 0.100 0.092 1.087 0.277 1.105 0.923
## ageyears -0.007 0.008 -0.904 0.366 0.993 0.979
## SEXnumeric -0.073 0.082 -0.887 0.375 0.930 0.791
## upper .95
## intercept 277.424
## arm 1.126
## TYPESTATUSnumeric 0.984
## day 1.105
## earlyacademicyear 1.031
## white 1.319
## structuraletiology 1.158
## priorepilepsy 1.219
## status 1.323
## ageyears 1.008
## SEXnumeric 1.092
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.694 0.412 8.972 0.000 40.196 17.936
## arm 0.066 0.200 0.329 0.742 1.068 0.722
## TYPESTATUSnumeric 0.471 0.209 2.250 0.024 1.601 1.063
## day 0.224 0.235 0.954 0.340 1.251 0.789
## earlyacademicyear 0.346 0.220 1.575 0.115 1.413 0.919
## white -0.202 0.226 -0.892 0.372 0.817 0.524
## structuraletiology 0.140 0.266 0.526 0.599 1.150 0.683
## priorepilepsy -0.032 0.238 -0.134 0.894 0.969 0.607
## status -0.180 0.249 -0.726 0.468 0.835 0.513
## ageyears 0.015 0.019 0.830 0.407 1.016 0.979
## SEXnumeric 0.159 0.210 0.755 0.450 1.172 0.776
## upper .95
## intercept 90.080
## arm 1.581
## TYPESTATUSnumeric 2.412
## day 1.985
## earlyacademicyear 2.173
## white 1.273
## structuraletiology 1.937
## priorepilepsy 1.545
## status 1.359
## ageyears 1.053
## SEXnumeric 1.769
Time to treatment in the hospital
# Patients in each category
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 46 | 60 |
## | 0.434 | 0.566 |
## |-----------|-----------|
##
##
##
##
# Time to first BZD
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 9.00 52.88 24.75 1440.00
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0)
## [1] 165.7452
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 106 106 9 6 15
# Figure time to first BZD
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 4.25 8.00 37.35 25.75 360.00
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 9.50 64.78 24.00 1440.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 46 24.9 23.6
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 60 30.6 31.8
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 0.0636 0.173
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 0.0473 0.173
##
## Chisq= 0.2 on 1 degrees of freedom, p= 0.7
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.6777943
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 106, number of events= 106
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.006995
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.324887
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.163687
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.240533
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.148264
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.007151
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy -0.051527
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.276420
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.009745
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.115834
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.007019
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.722609
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.177846
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.271927
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.862204
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.007177
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.949778
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.318402
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.990302
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.890623
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.211025
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.255315
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.218641
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.209979
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.218467
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.232073
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.257945
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.299264
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.019584
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.226932
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.033
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -1.272
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.749
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.146
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.679
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.031
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy -0.200
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.924
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.498
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.510
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.974
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.203
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.454
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.252
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.497
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.975
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.842
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.356
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.619
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.610
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.0070
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.7226
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.1778
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.2719
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.8622
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.0072
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.9498
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.3184
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9903
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.8906
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.9930
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.3839
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.8490
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.7862
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.1598
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.9929
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.0529
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.7585
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0098
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.1228
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.6659
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.4381
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.7673
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.8428
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.5619
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.6391
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.5729
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.7334
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9530
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.5709
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.523
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.192
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.808
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.920
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.323
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.587
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.575
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.370
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.029
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.390
##
## Concordance= 0.578 (se = 0.035 )
## Rsquare= 0.069 (max possible= 0.999 )
## Likelihood ratio test= 7.58 on 10 df, p=0.7
## Wald test = 7.53 on 10 df, p=0.7
## Score (logrank) test = 7.64 on 10 df, p=0.7
# Time to first non-BZD AED
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 22.25 40.50 99.53 85.25 1488.00
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0)
## [1] 212.08
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 106.0 106.0 40.5 29.0 51.0
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 20.25 44.50 79.70 86.75 503.00
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 23.00 36.00 114.73 79.75 1488.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 46 23.1 23.8
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 60 31.0 30.3
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 0.0188 0.0509
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 0.0148 0.0509
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.8
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.8214715
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 106, number of events= 106
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.113074
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.170027
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.472664
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.251552
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.259565
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.722228
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.009493
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.403198
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.029762
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.254245
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.119715
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.843642
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.604263
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.286019
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.771387
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 2.059016
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.009538
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.496603
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.970676
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.775501
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.212003
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.249145
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.231143
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.215040
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.229414
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.234215
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.263304
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.298991
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.019763
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.218367
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.533
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.682
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.045
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.170
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -1.131
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 3.084
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.036
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.349
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -1.506
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -1.164
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.59378
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.49496
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.04086 *
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.24208
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.25788
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.00205 **
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.97124
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.17749
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.13209
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.24430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.1197
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.8436
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.6043
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.2860
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.7714
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 2.0590
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.0095
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.4966
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9707
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.7755
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.8931
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.1853
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.6233
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.7776
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.2964
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.4857
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.9906
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.6682
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0302
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.2895
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.7390
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.5177
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.0198
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.8437
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.4920
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.3011
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.6026
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.8329
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9338
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.5055
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.697
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.375
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.524
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.960
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.209
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 3.259
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.691
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.689
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.009
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.190
##
## Concordance= 0.621 (se = 0.034 )
## Rsquare= 0.172 (max possible= 0.999 )
## Likelihood ratio test= 19.97 on 10 df, p=0.03
## Wald test = 19.89 on 10 df, p=0.03
## Score (logrank) test = 20.16 on 10 df, p=0.03
# Time to first CI
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 113.0 175.0 558.1 420.0 7200.0 55
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## 1)
##
## 55 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 51 51 175 122 253
# Figure time to first CI
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 121.0 210.0 386.6 462.0 2520.0 23
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.00 90.75 147.50 699.00 420.00 7200.00 32
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness, rho = 1)
##
## n=51, 55 observations deleted due to missingness.
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 23 10.9 12.7
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 28 15.3 13.5
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 0.240 0.702
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 0.225 0.702
##
## Chisq= 0.7 on 1 degrees of freedom, p= 0.4
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.4022339
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 51, number of events= 51
## (55 observations deleted due to missingness)
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.132425
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.544186
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.068633
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.136325
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.005572
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.151311
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.040558
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.795154
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.026320
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.197528
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.141594
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.580314
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.071044
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.146054
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.994443
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.859580
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.041392
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.214782
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.974023
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.218387
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.325070
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.363263
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.338336
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.313471
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.348243
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.331605
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.449628
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.549214
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.033234
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.307098
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.407
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -1.498
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.203
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.435
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.016
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.456
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.090
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.448
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.792
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.643
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.684
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.134
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.839
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.664
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.987
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.648
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.928
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.148
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.428
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.520
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.1416
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.5803
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.0710
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.1461
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.9944
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.8596
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.0414
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.2148
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9740
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.2184
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.8760
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.7232
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.9337
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.8726
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.0056
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.1634
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.9603
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.4515
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0267
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.8208
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.6037
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.2847
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.5518
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.6200
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.5025
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.4488
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.4314
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.7548
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9126
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.6674
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 2.159
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.183
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.079
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 2.119
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.968
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.646
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 2.514
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 6.499
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.040
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 2.224
##
## Concordance= 0.587 (se = 0.049 )
## Rsquare= 0.138 (max possible= 0.997 )
## Likelihood ratio test= 7.57 on 10 df, p=0.7
## Wald test = 7.53 on 10 df, p=0.7
## Score (logrank) test = 7.86 on 10 df, p=0.6
#### Recommendations and outliers in the hospital
# First BZD later than 20 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 72 | 34 |
## | 0.679 | 0.321 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 32 | 14 |
## | 0.696 | 0.304 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 60
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 40 | 20 |
## | 0.667 | 0.333 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore20min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore20min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.835
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4639872 2.8576593
## sample estimates:
## odds ratio
## 1.141421
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=20,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.083 -2.859 2.693 0.953
## RMST (arm=1)/(arm=0) 1.004 0.778 1.297 0.975
## RMTL (arm=1)/(arm=0) 1.026 0.751 1.400 0.873
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 9.791 2.189 4.473 0.000 5.501 14.082
## arm -0.083 1.416 -0.059 0.953 -2.859 2.693
## TYPESTATUSnumeric -0.945 1.590 -0.595 0.552 -4.061 2.170
## day -1.659 1.433 -1.158 0.247 -4.469 1.150
## earlyacademicyear -1.547 1.412 -1.096 0.273 -4.314 1.220
## white 0.925 1.542 0.600 0.549 -2.098 3.947
## structuraletiology -0.508 1.564 -0.325 0.745 -3.574 2.558
## priorepilepsy 2.277 1.688 1.349 0.177 -1.031 5.586
## status -1.631 1.802 -0.905 0.365 -5.163 1.901
## ageyears 0.231 0.140 1.655 0.098 -0.043 0.505
## SEXnumeric 1.257 1.435 0.876 0.381 -1.556 4.069
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.270 0.209 10.854 0.000 9.682 6.426
## arm 0.004 0.130 0.032 0.975 1.004 0.778
## TYPESTATUSnumeric -0.088 0.153 -0.578 0.564 0.916 0.679
## day -0.139 0.130 -1.072 0.284 0.870 0.674
## earlyacademicyear -0.136 0.133 -1.020 0.308 0.873 0.673
## white 0.074 0.149 0.499 0.618 1.077 0.804
## structuraletiology -0.041 0.145 -0.282 0.778 0.960 0.722
## priorepilepsy 0.190 0.148 1.285 0.199 1.210 0.905
## status -0.123 0.155 -0.792 0.428 0.884 0.652
## ageyears 0.020 0.012 1.638 0.101 1.020 0.996
## SEXnumeric 0.107 0.132 0.808 0.419 1.113 0.859
## upper .95
## intercept 14.589
## arm 1.297
## TYPESTATUSnumeric 1.235
## day 1.122
## earlyacademicyear 1.133
## white 1.443
## structuraletiology 1.276
## priorepilepsy 1.617
## status 1.199
## ageyears 1.044
## SEXnumeric 1.442
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.305 0.240 9.621 0.000 10.027 6.269
## arm 0.025 0.159 0.160 0.873 1.026 0.751
## TYPESTATUSnumeric 0.104 0.174 0.602 0.547 1.110 0.790
## day 0.203 0.165 1.228 0.219 1.225 0.886
## earlyacademicyear 0.184 0.157 1.172 0.241 1.202 0.884
## white -0.120 0.165 -0.724 0.469 0.887 0.642
## structuraletiology 0.065 0.173 0.374 0.708 1.067 0.760
## priorepilepsy -0.289 0.216 -1.336 0.182 0.749 0.491
## status 0.228 0.232 0.983 0.326 1.256 0.797
## ageyears -0.028 0.018 -1.595 0.111 0.972 0.939
## SEXnumeric -0.154 0.162 -0.955 0.340 0.857 0.625
## upper .95
## intercept 16.037
## arm 1.400
## TYPESTATUSnumeric 1.560
## day 1.692
## earlyacademicyear 1.636
## white 1.227
## structuraletiology 1.498
## priorepilepsy 1.144
## status 1.981
## ageyears 1.006
## SEXnumeric 1.176
# First BZD later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 87 | 19 |
## | 0.821 | 0.179 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 36 | 10 |
## | 0.783 | 0.217 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 60
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 51 | 9 |
## | 0.850 | 0.150 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore40min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore40min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.4467
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2061146 1.9456833
## sample estimates:
## odds ratio
## 0.6380838
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.511 -6.780 3.757 0.574
## RMST (arm=1)/(arm=0) 0.919 0.655 1.289 0.624
## RMTL (arm=1)/(arm=0) 1.069 0.859 1.331 0.550
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 14.834 4.509 3.290 0.001 5.997 23.671
## arm -1.511 2.688 -0.562 0.574 -6.780 3.757
## TYPESTATUSnumeric -4.986 2.664 -1.872 0.061 -10.207 0.235
## day -2.052 2.834 -0.724 0.469 -7.607 3.503
## earlyacademicyear -2.731 2.750 -0.993 0.321 -8.120 2.658
## white 2.201 3.024 0.728 0.467 -3.726 8.128
## structuraletiology 0.157 3.056 0.051 0.959 -5.833 6.147
## priorepilepsy 2.852 3.481 0.819 0.413 -3.970 9.674
## status -4.134 3.512 -1.177 0.239 -11.018 2.750
## ageyears 0.491 0.272 1.807 0.071 -0.042 1.023
## SEXnumeric 0.662 2.692 0.246 0.806 -4.614 5.937
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.668 0.302 8.832 0.000 14.417 7.975
## arm -0.085 0.173 -0.490 0.624 0.919 0.655
## TYPESTATUSnumeric -0.353 0.198 -1.781 0.075 0.703 0.477
## day -0.106 0.180 -0.587 0.557 0.900 0.633
## earlyacademicyear -0.171 0.183 -0.933 0.351 0.843 0.589
## white 0.127 0.205 0.620 0.535 1.135 0.760
## structuraletiology 0.021 0.195 0.110 0.912 1.022 0.697
## priorepilepsy 0.159 0.207 0.768 0.442 1.173 0.781
## status -0.223 0.220 -1.014 0.311 0.800 0.519
## ageyears 0.029 0.016 1.834 0.067 1.030 0.998
## SEXnumeric 0.027 0.174 0.156 0.876 1.027 0.731
## upper .95
## intercept 26.065
## arm 1.289
## TYPESTATUSnumeric 1.036
## day 1.280
## earlyacademicyear 1.207
## white 1.695
## structuraletiology 1.498
## priorepilepsy 1.760
## status 1.232
## ageyears 1.063
## SEXnumeric 1.444
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.214 0.185 17.381 0.000 24.885 17.319
## arm 0.067 0.112 0.598 0.550 1.069 0.859
## TYPESTATUSnumeric 0.197 0.106 1.855 0.064 1.218 0.989
## day 0.095 0.119 0.796 0.426 1.099 0.871
## earlyacademicyear 0.115 0.114 1.015 0.310 1.122 0.898
## white -0.098 0.123 -0.794 0.427 0.907 0.712
## structuraletiology -0.002 0.127 -0.013 0.989 0.998 0.778
## priorepilepsy -0.129 0.156 -0.829 0.407 0.879 0.647
## status 0.190 0.155 1.219 0.223 1.209 0.891
## ageyears -0.021 0.012 -1.720 0.085 0.979 0.956
## SEXnumeric -0.035 0.112 -0.311 0.756 0.966 0.776
## upper .95
## intercept 35.756
## arm 1.331
## TYPESTATUSnumeric 1.500
## day 1.388
## earlyacademicyear 1.402
## white 1.154
## structuraletiology 1.281
## priorepilepsy 1.193
## status 1.639
## ageyears 1.003
## SEXnumeric 1.202
# First BZD later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 91 | 15 |
## | 0.858 | 0.142 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 38 | 8 |
## | 0.826 | 0.174 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 60
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 53 | 7 |
## | 0.883 | 0.117 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.4158
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1777201 2.1803303
## sample estimates:
## odds ratio
## 0.6301883
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -3.168 -10.546 4.211 0.400
## RMST (arm=1)/(arm=0) 0.850 0.576 1.256 0.415
## RMTL (arm=1)/(arm=0) 1.081 0.901 1.297 0.400
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 21.594 6.713 3.217 0.001 8.436 34.751
## arm -3.168 3.765 -0.841 0.400 -10.546 4.211
## TYPESTATUSnumeric -7.746 3.520 -2.201 0.028 -14.645 -0.847
## day -2.471 4.080 -0.606 0.545 -10.468 5.526
## earlyacademicyear -5.314 3.970 -1.339 0.181 -13.096 2.467
## white 2.103 4.439 0.474 0.636 -6.597 10.802
## structuraletiology 0.356 4.318 0.083 0.934 -8.107 8.820
## priorepilepsy 1.234 5.055 0.244 0.807 -8.673 11.141
## status -5.587 4.687 -1.192 0.233 -14.773 3.598
## ageyears 0.653 0.394 1.660 0.097 -0.118 1.425
## SEXnumeric -0.539 3.918 -0.138 0.891 -8.218 7.139
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.046 0.355 8.585 0.000 21.038 10.495
## arm -0.162 0.199 -0.815 0.415 0.850 0.576
## TYPESTATUSnumeric -0.475 0.234 -2.026 0.043 0.622 0.393
## day -0.102 0.213 -0.478 0.633 0.903 0.595
## earlyacademicyear -0.284 0.221 -1.283 0.199 0.753 0.488
## white 0.095 0.245 0.387 0.698 1.100 0.680
## structuraletiology 0.035 0.232 0.151 0.880 1.036 0.657
## priorepilepsy 0.049 0.248 0.197 0.844 1.050 0.646
## status -0.268 0.261 -1.025 0.305 0.765 0.459
## ageyears 0.033 0.019 1.711 0.087 1.033 0.995
## SEXnumeric -0.045 0.209 -0.217 0.828 0.956 0.634
## upper .95
## intercept 42.174
## arm 1.256
## TYPESTATUSnumeric 0.985
## day 1.371
## earlyacademicyear 1.162
## white 1.777
## structuraletiology 1.633
## priorepilepsy 1.706
## status 1.276
## ageyears 1.072
## SEXnumeric 1.440
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.642 0.167 21.807 0.000 38.186 27.525
## arm 0.078 0.093 0.842 0.400 1.081 0.901
## TYPESTATUSnumeric 0.181 0.083 2.188 0.029 1.199 1.019
## day 0.066 0.101 0.655 0.513 1.068 0.876
## earlyacademicyear 0.130 0.098 1.332 0.183 1.139 0.940
## white -0.055 0.108 -0.512 0.608 0.946 0.766
## structuraletiology -0.005 0.106 -0.052 0.959 0.995 0.808
## priorepilepsy -0.034 0.130 -0.262 0.794 0.967 0.750
## status 0.143 0.117 1.217 0.224 1.153 0.916
## ageyears -0.016 0.010 -1.589 0.112 0.984 0.964
## SEXnumeric 0.009 0.096 0.095 0.924 1.009 0.836
## upper .95
## intercept 52.976
## arm 1.297
## TYPESTATUSnumeric 1.410
## day 1.303
## earlyacademicyear 1.380
## white 1.169
## structuraletiology 1.224
## priorepilepsy 1.246
## status 1.452
## ageyears 1.004
## SEXnumeric 1.218
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 53 | 53 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 22 | 24 |
## | 0.478 | 0.522 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 60
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 31 | 29 |
## | 0.517 | 0.483 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore40min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore40min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.8448
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3703187 1.9833858
## sample estimates:
## odds ratio
## 0.8587752
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.621 -5.480 2.237 0.410
## RMST (arm=1)/(arm=0) 0.949 0.836 1.077 0.414
## RMTL (arm=1)/(arm=0) 1.188 0.783 1.802 0.418
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 28.911 3.297 8.770 0.000 22.450 35.372
## arm -1.621 1.969 -0.824 0.410 -5.480 2.237
## TYPESTATUSnumeric 2.651 2.445 1.085 0.278 -2.140 7.443
## day -4.473 2.180 -2.052 0.040 -8.746 -0.201
## earlyacademicyear 0.017 2.127 0.008 0.994 -4.152 4.186
## white 3.735 2.172 1.719 0.086 -0.523 7.992
## structuraletiology -3.455 2.170 -1.592 0.111 -7.708 0.798
## priorepilepsy 1.694 2.482 0.682 0.495 -3.170 6.557
## status -2.928 2.804 -1.044 0.296 -8.424 2.568
## ageyears 0.438 0.207 2.117 0.034 0.033 0.844
## SEXnumeric 0.489 2.242 0.218 0.827 -3.905 4.882
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.358 0.112 30.116 0.000 28.738 23.096
## arm -0.053 0.065 -0.816 0.414 0.949 0.836
## TYPESTATUSnumeric 0.088 0.079 1.117 0.264 1.092 0.936
## day -0.148 0.073 -2.030 0.042 0.862 0.747
## earlyacademicyear 0.006 0.071 0.083 0.934 1.006 0.875
## white 0.127 0.075 1.692 0.091 1.136 0.980
## structuraletiology -0.118 0.076 -1.559 0.119 0.889 0.766
## priorepilepsy 0.052 0.079 0.659 0.510 1.053 0.903
## status -0.095 0.092 -1.033 0.301 0.909 0.760
## ageyears 0.014 0.007 2.106 0.035 1.014 1.001
## SEXnumeric 0.014 0.074 0.186 0.853 1.014 0.876
## upper .95
## intercept 35.758
## arm 1.077
## TYPESTATUSnumeric 1.275
## day 0.995
## earlyacademicyear 1.157
## white 1.316
## structuraletiology 1.031
## priorepilepsy 1.229
## status 1.089
## ageyears 1.027
## SEXnumeric 1.173
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.348 0.344 6.818 0.000 10.467 5.329
## arm 0.172 0.213 0.810 0.418 1.188 0.783
## TYPESTATUSnumeric -0.271 0.292 -0.926 0.354 0.763 0.430
## day 0.466 0.242 1.924 0.054 1.593 0.991
## earlyacademicyear 0.056 0.220 0.255 0.799 1.058 0.688
## white -0.358 0.219 -1.636 0.102 0.699 0.455
## structuraletiology 0.327 0.211 1.554 0.120 1.387 0.918
## priorepilepsy -0.213 0.326 -0.652 0.514 0.808 0.427
## status 0.333 0.331 1.006 0.314 1.396 0.729
## ageyears -0.055 0.029 -1.911 0.056 0.947 0.895
## SEXnumeric -0.074 0.232 -0.319 0.750 0.929 0.589
## upper .95
## intercept 20.558
## arm 1.802
## TYPESTATUSnumeric 1.353
## day 2.561
## earlyacademicyear 1.626
## white 1.073
## structuraletiology 2.095
## priorepilepsy 1.531
## status 2.673
## ageyears 1.001
## SEXnumeric 1.464
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 70 | 36 |
## | 0.660 | 0.340 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 30 | 16 |
## | 0.652 | 0.348 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 60
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 40 | 20 |
## | 0.667 | 0.333 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3870208 2.2911897
## sample estimates:
## odds ratio
## 0.9380757
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -3.609 -10.328 3.109 0.292
## RMST (arm=1)/(arm=0) 0.910 0.767 1.080 0.280
## RMTL (arm=1)/(arm=0) 1.182 0.844 1.654 0.330
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 38.778 5.697 6.806 0.000 27.611 49.944
## arm -3.609 3.428 -1.053 0.292 -10.328 3.109
## TYPESTATUSnumeric 1.003 4.302 0.233 0.816 -7.429 9.435
## day -8.555 3.701 -2.311 0.021 -15.809 -1.300
## earlyacademicyear 0.987 3.640 0.271 0.786 -6.147 8.121
## white 6.147 3.741 1.643 0.100 -1.185 13.480
## structuraletiology -7.831 3.569 -2.194 0.028 -14.825 -0.836
## priorepilepsy 1.394 4.310 0.323 0.746 -7.055 9.842
## status -5.182 4.801 -1.079 0.280 -14.591 4.228
## ageyears 0.694 0.346 2.005 0.045 0.016 1.373
## SEXnumeric 2.342 3.731 0.628 0.530 -4.969 9.654
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.647 0.150 24.390 0.000 38.351 28.609
## arm -0.094 0.087 -1.080 0.280 0.910 0.767
## TYPESTATUSnumeric 0.029 0.110 0.261 0.794 1.029 0.829
## day -0.224 0.097 -2.321 0.020 0.799 0.662
## earlyacademicyear 0.037 0.096 0.384 0.701 1.037 0.860
## white 0.167 0.102 1.643 0.100 1.182 0.968
## structuraletiology -0.216 0.100 -2.171 0.030 0.805 0.663
## priorepilepsy 0.029 0.107 0.267 0.790 1.029 0.834
## status -0.136 0.126 -1.081 0.280 0.872 0.681
## ageyears 0.017 0.008 2.006 0.045 1.017 1.000
## SEXnumeric 0.058 0.096 0.606 0.544 1.060 0.879
## upper .95
## intercept 51.410
## arm 1.080
## TYPESTATUSnumeric 1.278
## day 0.966
## earlyacademicyear 1.251
## white 1.443
## structuraletiology 0.979
## priorepilepsy 1.269
## status 1.117
## ageyears 1.034
## SEXnumeric 1.278
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.015 0.275 10.982 0.000 20.397 11.908
## arm 0.167 0.172 0.974 0.330 1.182 0.844
## TYPESTATUSnumeric -0.033 0.211 -0.157 0.875 0.967 0.640
## day 0.403 0.190 2.114 0.035 1.496 1.030
## earlyacademicyear -0.010 0.173 -0.059 0.953 0.990 0.705
## white -0.268 0.173 -1.550 0.121 0.765 0.545
## structuraletiology 0.335 0.162 2.067 0.039 1.398 1.017
## priorepilepsy -0.082 0.233 -0.351 0.726 0.921 0.583
## status 0.241 0.236 1.020 0.308 1.272 0.801
## ageyears -0.038 0.020 -1.879 0.060 0.963 0.926
## SEXnumeric -0.118 0.181 -0.655 0.513 0.888 0.623
## upper .95
## intercept 34.938
## arm 1.654
## TYPESTATUSnumeric 1.463
## day 2.173
## earlyacademicyear 1.389
## white 1.073
## structuraletiology 1.921
## priorepilepsy 1.456
## status 2.019
## ageyears 1.002
## SEXnumeric 1.266
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 89 | 17 |
## | 0.840 | 0.160 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 39 | 7 |
## | 0.848 | 0.152 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 60
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 50 | 10 |
## | 0.833 | 0.167 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore120min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore120min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3456596 3.7818722
## sample estimates:
## odds ratio
## 1.113139
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=120,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -6.343 -20.061 7.376 0.365
## RMST (arm=1)/(arm=0) 0.889 0.690 1.144 0.360
## RMTL (arm=1)/(arm=0) 1.102 0.889 1.365 0.376
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 58.931 12.551 4.695 0.000 34.331 83.530
## arm -6.343 6.999 -0.906 0.365 -20.061 7.376
## TYPESTATUSnumeric -10.959 8.013 -1.368 0.171 -26.664 4.747
## day -16.539 7.451 -2.220 0.026 -31.143 -1.935
## earlyacademicyear -1.965 7.726 -0.254 0.799 -17.108 13.178
## white 9.463 7.348 1.288 0.198 -4.939 23.866
## structuraletiology -17.087 7.053 -2.423 0.015 -30.910 -3.263
## priorepilepsy 1.750 9.314 0.188 0.851 -16.505 20.005
## status -9.308 10.198 -0.913 0.361 -29.296 10.679
## ageyears 1.410 0.718 1.964 0.050 0.003 2.818
## SEXnumeric 6.884 7.623 0.903 0.367 -8.058 21.825
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.064 0.235 17.304 0.000 58.197 36.728
## arm -0.118 0.129 -0.916 0.360 0.889 0.690
## TYPESTATUSnumeric -0.211 0.164 -1.287 0.198 0.810 0.587
## day -0.307 0.138 -2.228 0.026 0.735 0.561
## earlyacademicyear -0.013 0.151 -0.088 0.930 0.987 0.734
## white 0.188 0.145 1.296 0.195 1.207 0.908
## structuraletiology -0.354 0.146 -2.419 0.016 0.702 0.527
## priorepilepsy 0.007 0.167 0.045 0.964 1.007 0.726
## status -0.172 0.197 -0.873 0.382 0.842 0.572
## ageyears 0.024 0.012 1.962 0.050 1.024 1.000
## SEXnumeric 0.122 0.138 0.882 0.378 1.129 0.862
## upper .95
## intercept 92.214
## arm 1.144
## TYPESTATUSnumeric 1.117
## day 0.964
## earlyacademicyear 1.326
## white 1.605
## structuraletiology 0.935
## priorepilepsy 1.399
## status 1.239
## ageyears 1.048
## SEXnumeric 1.480
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.101 0.193 21.243 0.000 60.415 41.381
## arm 0.097 0.109 0.886 0.376 1.102 0.889
## TYPESTATUSnumeric 0.165 0.117 1.413 0.158 1.179 0.938
## day 0.255 0.121 2.099 0.036 1.290 1.017
## earlyacademicyear 0.044 0.116 0.378 0.706 1.045 0.832
## white -0.139 0.110 -1.261 0.207 0.870 0.701
## structuraletiology 0.242 0.105 2.297 0.022 1.274 1.036
## priorepilepsy -0.039 0.151 -0.260 0.794 0.961 0.715
## status 0.143 0.158 0.910 0.363 1.154 0.847
## ageyears -0.024 0.012 -1.882 0.060 0.977 0.953
## SEXnumeric -0.109 0.119 -0.917 0.359 0.896 0.709
## upper .95
## intercept 88.202
## arm 1.365
## TYPESTATUSnumeric 1.483
## day 1.636
## earlyacademicyear 1.313
## white 1.080
## structuraletiology 1.566
## priorepilepsy 1.292
## status 1.572
## ageyears 1.001
## SEXnumeric 1.132
# First CI later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 51
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 45 |
## | 0.118 | 0.882 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 20 |
## | 0.130 | 0.870 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 28
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 25 |
## | 0.107 | 0.893 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1499791 10.3332141
## sample estimates:
## odds ratio
## 1.244498
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 2.856 -3.935 9.647 0.410
## RMST (arm=1)/(arm=0) 1.052 0.932 1.187 0.411
## RMTL (arm=1)/(arm=0) 0.036 0.000 7.667 0.224
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 52.621 5.122 10.273 0.000 42.582 62.661
## arm 2.856 3.465 0.824 0.410 -3.935 9.647
## TYPESTATUSnumeric 3.849 3.415 1.127 0.260 -2.844 10.542
## day -6.217 3.277 -1.897 0.058 -12.640 0.206
## earlyacademicyear 2.342 3.253 0.720 0.472 -4.034 8.717
## white 1.304 3.965 0.329 0.742 -6.467 9.075
## structuraletiology -2.219 4.221 -0.526 0.599 -10.492 6.055
## priorepilepsy 4.686 3.071 1.526 0.127 -1.332 10.705
## status 0.659 1.918 0.343 0.731 -3.100 4.418
## ageyears 0.254 0.268 0.949 0.343 -0.271 0.779
## SEXnumeric 1.125 4.257 0.264 0.792 -7.218 9.468
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.960 0.098 40.371 0.000 52.458 43.283
## arm 0.051 0.062 0.823 0.411 1.052 0.932
## TYPESTATUSnumeric 0.068 0.062 1.091 0.275 1.070 0.948
## day -0.109 0.061 -1.786 0.074 0.897 0.796
## earlyacademicyear 0.042 0.060 0.710 0.478 1.043 0.928
## white 0.026 0.075 0.344 0.731 1.026 0.886
## structuraletiology -0.040 0.078 -0.515 0.607 0.961 0.825
## priorepilepsy 0.082 0.057 1.448 0.147 1.085 0.972
## status 0.012 0.035 0.339 0.734 1.012 0.946
## ageyears 0.005 0.005 0.922 0.357 1.005 0.995
## SEXnumeric 0.020 0.076 0.260 0.795 1.020 0.879
## upper .95
## intercept 63.578
## arm 1.187
## TYPESTATUSnumeric 1.208
## day 1.011
## earlyacademicyear 1.173
## white 1.188
## structuraletiology 1.119
## priorepilepsy 1.212
## status 1.083
## ageyears 1.014
## SEXnumeric 1.184
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept -30.089 15.467 -1.945 0.052 0.000000e+00 0.000
## arm -3.336 2.742 -1.217 0.224 3.600000e-02 0.000
## TYPESTATUSnumeric -6.922 7.027 -0.985 0.325 1.000000e-03 0.000
## day 30.429 12.383 2.457 0.014 1.640917e+13 472.733
## earlyacademicyear 2.186 2.707 0.807 0.420 8.895000e+00 0.044
## white 7.312 7.441 0.983 0.326 1.497991e+03 0.001
## structuraletiology 6.785 6.175 1.099 0.272 8.847070e+02 0.005
## priorepilepsy -25.509 5.790 -4.406 0.000 0.000000e+00 0.000
## status 1.394 2.089 0.667 0.505 4.030000e+00 0.067
## ageyears -0.299 0.279 -1.071 0.284 7.420000e-01 0.430
## SEXnumeric -3.688 3.298 -1.119 0.263 2.500000e-02 0.000
## upper .95
## intercept 1.254000e+00
## arm 7.667000e+00
## TYPESTATUSnumeric 9.452930e+02
## day 5.695835e+23
## earlyacademicyear 1.793332e+03
## white 3.232650e+09
## structuraletiology 1.596780e+08
## priorepilepsy 0.000000e+00
## status 2.417270e+02
## ageyears 1.281000e+00
## SEXnumeric 1.603300e+01
# First CI later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 51
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 17 | 34 |
## | 0.333 | 0.667 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 17 |
## | 0.261 | 0.739 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 28
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 11 | 17 |
## | 0.393 | 0.607 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore120min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore120min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.381
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1343347 2.0889696
## sample estimates:
## odds ratio
## 0.5519603
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.805 -19.283 13.673 0.739
## RMST (arm=1)/(arm=0) 0.974 0.834 1.137 0.734
## RMTL (arm=1)/(arm=0) 1.204 0.342 4.240 0.773
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 101.070 11.623 8.695 0.000 78.289 123.851
## arm -2.805 8.407 -0.334 0.739 -19.283 13.673
## TYPESTATUSnumeric 11.275 8.492 1.328 0.184 -5.368 27.918
## day -10.569 8.453 -1.250 0.211 -27.137 6.000
## earlyacademicyear 3.885 8.447 0.460 0.646 -12.671 20.442
## white -2.514 9.540 -0.264 0.792 -21.212 16.183
## structuraletiology -5.011 10.431 -0.480 0.631 -25.456 15.434
## priorepilepsy 6.419 9.071 0.708 0.479 -11.360 24.198
## status 1.494 8.585 0.174 0.862 -15.333 18.320
## ageyears 1.000 0.661 1.513 0.130 -0.295 2.295
## SEXnumeric 3.017 9.489 0.318 0.751 -15.581 21.616
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.610 0.118 39.166 0.000 100.519 79.809
## arm -0.027 0.079 -0.340 0.734 0.974 0.834
## TYPESTATUSnumeric 0.109 0.083 1.311 0.190 1.115 0.948
## day -0.100 0.084 -1.195 0.232 0.905 0.768
## earlyacademicyear 0.039 0.082 0.474 0.636 1.039 0.886
## white -0.022 0.096 -0.229 0.819 0.978 0.811
## structuraletiology -0.048 0.103 -0.463 0.643 0.953 0.779
## priorepilepsy 0.060 0.088 0.682 0.495 1.062 0.894
## status 0.015 0.080 0.192 0.848 1.016 0.867
## ageyears 0.010 0.007 1.463 0.144 1.010 0.997
## SEXnumeric 0.028 0.092 0.302 0.762 1.028 0.859
## upper .95
## intercept 126.604
## arm 1.137
## TYPESTATUSnumeric 1.312
## day 1.066
## earlyacademicyear 1.219
## white 1.180
## structuraletiology 1.167
## priorepilepsy 1.262
## status 1.189
## ageyears 1.023
## SEXnumeric 1.230
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.600 0.712 3.652 0.000 13.464 3.336
## arm 0.186 0.642 0.289 0.773 1.204 0.342
## TYPESTATUSnumeric -0.765 0.701 -1.092 0.275 0.465 0.118
## day 0.835 0.590 1.416 0.157 2.306 0.726
## earlyacademicyear -0.186 0.642 -0.290 0.772 0.830 0.236
## white 0.357 0.517 0.691 0.490 1.429 0.519
## structuraletiology 0.380 0.645 0.589 0.556 1.462 0.413
## priorepilepsy -0.487 0.660 -0.738 0.461 0.615 0.169
## status -0.103 0.821 -0.126 0.900 0.902 0.181
## ageyears -0.074 0.053 -1.395 0.163 0.928 0.836
## SEXnumeric -0.270 0.595 -0.454 0.650 0.764 0.238
## upper .95
## intercept 54.346
## arm 4.240
## TYPESTATUSnumeric 1.837
## day 7.326
## earlyacademicyear 2.922
## white 3.933
## structuraletiology 5.181
## priorepilepsy 2.239
## status 4.505
## ageyears 1.031
## SEXnumeric 2.449
# First CI later than 240 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 51
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 33 | 18 |
## | 0.647 | 0.353 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 13 | 10 |
## | 0.565 | 0.435 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 28
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 8 |
## | 0.714 | 0.286 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore240min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore240min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.3784
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1383207 1.9334817
## sample estimates:
## odds ratio
## 0.5268577
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -23.387 -64.120 17.346 0.260
## RMST (arm=1)/(arm=0) 0.867 0.679 1.108 0.255
## RMTL (arm=1)/(arm=0) 1.388 0.751 2.567 0.296
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 183.537 30.975 5.925 0.000 122.826 244.248
## arm -23.387 20.782 -1.125 0.260 -64.120 17.346
## TYPESTATUSnumeric -2.385 22.115 -0.108 0.914 -45.731 40.960
## day -21.428 21.765 -0.984 0.325 -64.087 21.231
## earlyacademicyear 3.846 21.896 0.176 0.861 -39.069 46.761
## white -17.596 22.494 -0.782 0.434 -61.684 26.492
## structuraletiology 8.556 26.206 0.326 0.744 -42.807 59.918
## priorepilepsy -9.487 30.842 -0.308 0.758 -69.937 50.963
## status -10.921 29.020 -0.376 0.707 -67.799 45.956
## ageyears 3.113 1.663 1.872 0.061 -0.147 6.374
## SEXnumeric -1.786 22.440 -0.080 0.937 -45.767 42.195
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.208 0.189 27.578 0.000 182.778 126.231
## arm -0.142 0.125 -1.139 0.255 0.867 0.679
## TYPESTATUSnumeric -0.008 0.144 -0.053 0.958 0.992 0.748
## day -0.132 0.135 -0.983 0.326 0.876 0.673
## earlyacademicyear 0.026 0.133 0.193 0.847 1.026 0.791
## white -0.103 0.137 -0.756 0.450 0.902 0.690
## structuraletiology 0.056 0.161 0.345 0.730 1.057 0.771
## priorepilepsy -0.068 0.194 -0.349 0.727 0.935 0.639
## status -0.054 0.189 -0.289 0.773 0.947 0.654
## ageyears 0.018 0.010 1.829 0.067 1.018 0.999
## SEXnumeric -0.008 0.136 -0.058 0.954 0.992 0.760
## upper .95
## intercept 264.655
## arm 1.108
## TYPESTATUSnumeric 1.317
## day 1.141
## earlyacademicyear 1.331
## white 1.179
## structuraletiology 1.449
## priorepilepsy 1.367
## status 1.370
## ageyears 1.038
## SEXnumeric 1.296
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.005 0.427 9.382 0.000 54.881 23.770
## arm 0.328 0.314 1.045 0.296 1.388 0.751
## TYPESTATUSnumeric 0.066 0.266 0.249 0.803 1.069 0.634
## day 0.291 0.294 0.990 0.322 1.338 0.752
## earlyacademicyear -0.035 0.305 -0.115 0.909 0.966 0.531
## white 0.255 0.312 0.816 0.414 1.291 0.700
## structuraletiology -0.096 0.349 -0.274 0.784 0.909 0.458
## priorepilepsy 0.087 0.410 0.213 0.831 1.091 0.488
## status 0.201 0.368 0.546 0.585 1.223 0.594
## ageyears -0.048 0.028 -1.720 0.086 0.953 0.903
## SEXnumeric 0.035 0.315 0.113 0.910 1.036 0.559
## upper .95
## intercept 126.709
## arm 2.567
## TYPESTATUSnumeric 1.800
## day 2.381
## earlyacademicyear 1.756
## white 2.381
## structuraletiology 1.802
## priorepilepsy 2.439
## status 2.516
## ageyears 1.007
## SEXnumeric 1.920
Time to treatment sensitivity analysis 1: comparison 2011-2016 versus 2017-2019
# Create variable awareness of delays 2017 in time to treatment
pSERG$awareness2017 <- ifelse(pSERG$yearSE >= 2017, 1, 0)
CrossTable(pSERG$awareness2017)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 237 | 91 |
## | 0.723 | 0.277 |
## |-----------|-----------|
##
##
##
##
## ALL PATIENTS
# Time to first BZD
summary(pSERG$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 17.00 63.74 45.00 1440.00
sd(pSERG$BZDTIME.0)
## [1] 157.5196
survfit(Surv(pSERG$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$BZDTIME.0) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## 328 328 17 14 20
# Figure time to first BZD
plot(survfit(Surv(pSERG$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$awareness2017 == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 17.00 59.53 48.00 1264.00
summary(pSERG[which(pSERG$awareness2017 == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 5.0 15.0 74.7 36.0 1440.0
survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho = 1)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 151 76.8 81.3 0.245 0.733
## pSERG$awareness=1 177 94.1 89.6 0.223 0.733
##
## Chisq= 0.7 on 1 degrees of freedom, p= 0.4
pchisq(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.3918653
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness2017 + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$BZDTIME.0) ~ pSERG$awareness2017 +
## pSERG$TYPESTATUS + pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 328, number of events= 328
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness2017 -0.082672 0.920653 0.128813 -0.642 0.52101
## pSERG$TYPESTATUSintermittent -0.399841 0.670426 0.128163 -3.120 0.00181
## pSERG$HOSPITALONSETyes 0.376731 1.457512 0.125754 2.996 0.00274
## pSERG$day 0.101469 1.106796 0.115304 0.880 0.37885
## pSERG$earlyacademicyear 0.186072 1.204509 0.112069 1.660 0.09685
## pSERG$white 0.010736 1.010794 0.122555 0.088 0.93019
## pSERG$structuraletiology 0.054302 1.055804 0.135510 0.401 0.68862
## pSERG$priorepilepsy 0.019734 1.019930 0.124593 0.158 0.87415
## pSERG$status 0.500398 1.649378 0.157600 3.175 0.00150
## pSERG$ageyears -0.003008 0.996997 0.011126 -0.270 0.78691
## pSERG$SEXmale 0.070919 1.073494 0.116168 0.610 0.54154
##
## pSERG$awareness2017
## pSERG$TYPESTATUSintermittent **
## pSERG$HOSPITALONSETyes **
## pSERG$day
## pSERG$earlyacademicyear .
## pSERG$white
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status **
## pSERG$ageyears
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness2017 0.9207 1.0862 0.7152 1.1851
## pSERG$TYPESTATUSintermittent 0.6704 1.4916 0.5215 0.8619
## pSERG$HOSPITALONSETyes 1.4575 0.6861 1.1391 1.8649
## pSERG$day 1.1068 0.9035 0.8829 1.3874
## pSERG$earlyacademicyear 1.2045 0.8302 0.9670 1.5004
## pSERG$white 1.0108 0.9893 0.7950 1.2852
## pSERG$structuraletiology 1.0558 0.9471 0.8095 1.3770
## pSERG$priorepilepsy 1.0199 0.9805 0.7989 1.3020
## pSERG$status 1.6494 0.6063 1.2111 2.2463
## pSERG$ageyears 0.9970 1.0030 0.9755 1.0190
## pSERG$SEXmale 1.0735 0.9315 0.8549 1.3480
##
## Concordance= 0.615 (se = 0.02 )
## Rsquare= 0.107 (max possible= 1 )
## Likelihood ratio test= 37.03 on 11 df, p=1e-04
## Wald test = 39.1 on 11 df, p=5e-05
## Score (logrank) test = 40.01 on 11 df, p=4e-05
# Time to first non-BZD AED
summary(pSERG$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 33.75 65.50 162.60 150.00 4320.00
sd(pSERG$AEDTIME.0)
## [1] 333.9342
survfit(Surv(pSERG$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$AEDTIME.0) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## 328.0 328.0 65.5 60.0 77.0
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$awareness2017 == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 35.0 65.0 165.2 150.0 4320.0
summary(pSERG[which(pSERG$awareness2017 == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 31.5 66.0 155.9 151.5 1488.0
survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho = 1)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 151 75.4 77.2 0.0412 0.117
## pSERG$awareness=1 177 90.4 88.6 0.0359 0.117
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
pchisq(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.7328349
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness2017 + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$AEDTIME.0) ~ pSERG$awareness2017 +
## pSERG$TYPESTATUS + pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 328, number of events= 328
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness2017 -0.08924 0.91463 0.12971 -0.688 0.4915
## pSERG$TYPESTATUSintermittent -0.55775 0.57250 0.12735 -4.379 1.19e-05
## pSERG$HOSPITALONSETyes 0.69201 1.99772 0.12395 5.583 2.36e-08
## pSERG$day 0.24181 1.27355 0.11596 2.085 0.0370
## pSERG$earlyacademicyear 0.14622 1.15745 0.11358 1.287 0.1980
## pSERG$white -0.02534 0.97498 0.11944 -0.212 0.8320
## pSERG$structuraletiology 0.26884 1.30845 0.13265 2.027 0.0427
## pSERG$priorepilepsy -0.07114 0.93133 0.12429 -0.572 0.5670
## pSERG$status 0.27849 1.32113 0.15704 1.773 0.0762
## pSERG$ageyears -0.02367 0.97660 0.01113 -2.126 0.0335
## pSERG$SEXmale 0.08118 1.08457 0.11694 0.694 0.4876
##
## pSERG$awareness2017
## pSERG$TYPESTATUSintermittent ***
## pSERG$HOSPITALONSETyes ***
## pSERG$day *
## pSERG$earlyacademicyear
## pSERG$white
## pSERG$structuraletiology *
## pSERG$priorepilepsy
## pSERG$status .
## pSERG$ageyears *
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness2017 0.9146 1.0933 0.7093 1.1794
## pSERG$TYPESTATUSintermittent 0.5725 1.7467 0.4460 0.7348
## pSERG$HOSPITALONSETyes 1.9977 0.5006 1.5669 2.5471
## pSERG$day 1.2735 0.7852 1.0146 1.5985
## pSERG$earlyacademicyear 1.1574 0.8640 0.9265 1.4460
## pSERG$white 0.9750 1.0257 0.7715 1.2321
## pSERG$structuraletiology 1.3084 0.7643 1.0089 1.6970
## pSERG$priorepilepsy 0.9313 1.0737 0.7300 1.1882
## pSERG$status 1.3211 0.7569 0.9711 1.7973
## pSERG$ageyears 0.9766 1.0240 0.9555 0.9982
## pSERG$SEXmale 1.0846 0.9220 0.8624 1.3640
##
## Concordance= 0.654 (se = 0.019 )
## Rsquare= 0.185 (max possible= 1 )
## Likelihood ratio test= 66.9 on 11 df, p=5e-10
## Wald test = 68.9 on 11 df, p=2e-10
## Score (logrank) test = 70.36 on 11 df, p=1e-10
# Time to first CI
summary(pSERG$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 113.5 173.5 523.7 543.2 7200.0 176
sd(pSERG$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$CONTTIME.0) ~ 1)
##
## 176 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 152 152 174 154 230
# Figure time to first CI
plot(survfit(Surv(pSERG$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$awareness2017 == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 121.0 180.0 499.1 539.0 7200.0 122
summary(pSERG[which(pSERG$awareness2017 == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 85.0 140.0 600.4 540.0 6003.0 54
survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$CONTTIME.0) ~ pSERG$awareness,
## rho = 1)
##
## n=152, 176 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 68 32.2 36.5 0.516 1.48
## pSERG$awareness=1 84 44.6 40.3 0.468 1.48
##
## Chisq= 1.5 on 1 degrees of freedom, p= 0.2
pchisq(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.2236165
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness2017 + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$CONTTIME.0) ~ pSERG$awareness2017 +
## pSERG$TYPESTATUS + pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 152, number of events= 152
## (176 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness2017 -0.047674 0.953444 0.203674 -0.234 0.8149
## pSERG$TYPESTATUSintermittent -0.314521 0.730138 0.192225 -1.636 0.1018
## pSERG$HOSPITALONSETyes 0.115856 1.122834 0.186605 0.621 0.5347
## pSERG$day -0.009578 0.990467 0.175440 -0.055 0.9565
## pSERG$earlyacademicyear 0.243513 1.275723 0.177728 1.370 0.1706
## pSERG$white -0.348364 0.705842 0.193028 -1.805 0.0711
## pSERG$structuraletiology 0.198861 1.220012 0.206875 0.961 0.3364
## pSERG$priorepilepsy 0.201924 1.223755 0.200729 1.006 0.3144
## pSERG$status 0.120557 1.128125 0.231689 0.520 0.6028
## pSERG$ageyears -0.001996 0.998006 0.016978 -0.118 0.9064
## pSERG$SEXmale 0.191114 1.210598 0.176706 1.082 0.2795
##
## pSERG$awareness2017
## pSERG$TYPESTATUSintermittent
## pSERG$HOSPITALONSETyes
## pSERG$day
## pSERG$earlyacademicyear
## pSERG$white .
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status
## pSERG$ageyears
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness2017 0.9534 1.0488 0.6396 1.421
## pSERG$TYPESTATUSintermittent 0.7301 1.3696 0.5009 1.064
## pSERG$HOSPITALONSETyes 1.1228 0.8906 0.7789 1.619
## pSERG$day 0.9905 1.0096 0.7023 1.397
## pSERG$earlyacademicyear 1.2757 0.7839 0.9005 1.807
## pSERG$white 0.7058 1.4167 0.4835 1.030
## pSERG$structuraletiology 1.2200 0.8197 0.8133 1.830
## pSERG$priorepilepsy 1.2238 0.8172 0.8257 1.814
## pSERG$status 1.1281 0.8864 0.7164 1.777
## pSERG$ageyears 0.9980 1.0020 0.9653 1.032
## pSERG$SEXmale 1.2106 0.8260 0.8562 1.712
##
## Concordance= 0.566 (se = 0.028 )
## Rsquare= 0.07 (max possible= 1 )
## Likelihood ratio test= 11.05 on 11 df, p=0.4
## Wald test = 11.31 on 11 df, p=0.4
## Score (logrank) test = 11.37 on 11 df, p=0.4
# First BZD later than 20 minutes
CrossTable(pSERG$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 187 | 141 |
## | 0.570 | 0.430 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 237
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 134 | 103 |
## | 0.565 | 0.435 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 53 | 38 |
## | 0.582 | 0.418 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore20min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore20min and pSERG$awareness2017
## p-value = 0.8044
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5533601 1.5638451
## sample estimates:
## odds ratio
## 0.9329985
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=20,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.272 -2.011 1.467 0.759
## RMST (arm=1)/(arm=0) 0.984 0.861 1.124 0.808
## RMTL (arm=1)/(arm=0) 1.057 0.821 1.360 0.669
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 16.269 1.203 13.529 0.000 13.912 18.626
## arm -0.272 0.887 -0.306 0.759 -2.011 1.467
## TYPESTATUSnumeric -0.474 0.806 -0.588 0.556 -2.053 1.105
## HOSPITALONSETnumeric -3.323 0.876 -3.795 0.000 -5.039 -1.607
## day -0.739 0.780 -0.947 0.343 -2.267 0.789
## earlyacademicyear -1.183 0.775 -1.526 0.127 -2.703 0.336
## white -0.045 0.792 -0.056 0.955 -1.597 1.508
## structuraletiology -0.673 0.921 -0.731 0.465 -2.479 1.132
## priorepilepsy -0.667 0.838 -0.796 0.426 -2.309 0.975
## status -3.163 1.139 -2.778 0.005 -5.395 -0.932
## ageyears 0.044 0.078 0.559 0.576 -0.110 0.198
## SEXnumeric 0.439 0.787 0.557 0.578 -1.104 1.981
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.805 0.090 31.312 0.000 16.527 13.866
## arm -0.016 0.068 -0.242 0.808 0.984 0.861
## TYPESTATUSnumeric -0.033 0.061 -0.547 0.585 0.967 0.858
## HOSPITALONSETnumeric -0.265 0.074 -3.578 0.000 0.767 0.664
## day -0.057 0.058 -0.985 0.325 0.944 0.843
## earlyacademicyear -0.087 0.059 -1.485 0.138 0.917 0.817
## white -0.005 0.059 -0.088 0.930 0.995 0.886
## structuraletiology -0.051 0.071 -0.717 0.474 0.950 0.827
## priorepilepsy -0.053 0.061 -0.869 0.385 0.949 0.842
## status -0.265 0.101 -2.615 0.009 0.767 0.629
## ageyears 0.003 0.006 0.570 0.569 1.003 0.992
## SEXnumeric 0.033 0.059 0.561 0.575 1.034 0.921
## upper .95
## intercept 19.700
## arm 1.124
## TYPESTATUSnumeric 1.090
## HOSPITALONSETnumeric 0.887
## day 1.058
## earlyacademicyear 1.028
## white 1.117
## structuraletiology 1.092
## priorepilepsy 1.069
## status 0.936
## ageyears 1.015
## SEXnumeric 1.161
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.429 0.190 7.521 0.000 4.173 2.876
## arm 0.055 0.129 0.428 0.669 1.057 0.821
## TYPESTATUSnumeric 0.083 0.120 0.691 0.490 1.086 0.859
## HOSPITALONSETnumeric 0.462 0.120 3.843 0.000 1.587 1.254
## day 0.105 0.121 0.870 0.384 1.111 0.877
## earlyacademicyear 0.185 0.118 1.578 0.115 1.204 0.956
## white -0.002 0.121 -0.014 0.989 0.998 0.788
## structuraletiology 0.099 0.133 0.745 0.456 1.104 0.851
## priorepilepsy 0.090 0.138 0.650 0.516 1.094 0.834
## status 0.407 0.146 2.786 0.005 1.502 1.128
## ageyears -0.007 0.012 -0.534 0.593 0.993 0.969
## SEXnumeric -0.065 0.120 -0.537 0.591 0.938 0.741
## upper .95
## intercept 6.056
## arm 1.360
## TYPESTATUSnumeric 1.375
## HOSPITALONSETnumeric 2.008
## day 1.408
## earlyacademicyear 1.516
## white 1.265
## structuraletiology 1.432
## priorepilepsy 1.435
## status 2.000
## ageyears 1.018
## SEXnumeric 1.187
# First BZD later than 40 minutes
CrossTable(pSERG$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 238 | 90 |
## | 0.726 | 0.274 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 237
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 168 | 69 |
## | 0.709 | 0.291 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 70 | 21 |
## | 0.769 | 0.231 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore40min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore40min and pSERG$awareness2017
## p-value = 0.3335
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3944209 1.3171488
## sample estimates:
## odds ratio
## 0.7311137
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=40,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.719 -4.214 2.776 0.687
## RMST (arm=1)/(arm=0) 0.971 0.812 1.161 0.750
## RMTL (arm=1)/(arm=0) 1.043 0.878 1.240 0.628
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 25.570 2.439 10.486 0.000 20.790 30.349
## arm -0.719 1.783 -0.403 0.687 -4.214 2.776
## TYPESTATUSnumeric -3.816 1.560 -2.447 0.014 -6.873 -0.760
## HOSPITALONSETnumeric -7.116 1.681 -4.233 0.000 -10.411 -3.821
## day -0.954 1.611 -0.592 0.554 -4.111 2.203
## earlyacademicyear -2.171 1.572 -1.381 0.167 -5.252 0.910
## white 0.770 1.630 0.473 0.636 -2.424 3.965
## structuraletiology 0.438 1.912 0.229 0.819 -3.310 4.186
## priorepilepsy 0.180 1.746 0.103 0.918 -3.243 3.603
## status -6.864 2.174 -3.158 0.002 -11.124 -2.603
## ageyears 0.099 0.158 0.625 0.532 -0.211 0.409
## SEXnumeric 0.067 1.617 0.041 0.967 -3.103 3.237
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.259 0.120 27.187 0.000 26.021 20.573
## arm -0.029 0.091 -0.319 0.750 0.971 0.812
## TYPESTATUSnumeric -0.190 0.082 -2.319 0.020 0.827 0.705
## HOSPITALONSETnumeric -0.378 0.097 -3.883 0.000 0.685 0.566
## day -0.049 0.078 -0.621 0.534 0.952 0.817
## earlyacademicyear -0.105 0.079 -1.333 0.182 0.900 0.772
## white 0.035 0.081 0.428 0.668 1.035 0.883
## structuraletiology 0.024 0.094 0.259 0.796 1.025 0.852
## priorepilepsy 0.001 0.082 0.007 0.995 1.001 0.853
## status -0.384 0.136 -2.826 0.005 0.681 0.522
## ageyears 0.005 0.008 0.625 0.532 1.005 0.990
## SEXnumeric 0.006 0.079 0.072 0.943 1.006 0.861
## upper .95
## intercept 32.912
## arm 1.161
## TYPESTATUSnumeric 0.971
## HOSPITALONSETnumeric 0.829
## day 1.111
## earlyacademicyear 1.051
## white 1.214
## structuraletiology 1.232
## priorepilepsy 1.174
## status 0.889
## ageyears 1.020
## SEXnumeric 1.175
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.698 0.128 21.129 0.000 14.853 11.564
## arm 0.043 0.088 0.485 0.628 1.043 0.878
## TYPESTATUSnumeric 0.194 0.077 2.531 0.011 1.214 1.045
## HOSPITALONSETnumeric 0.344 0.080 4.286 0.000 1.411 1.205
## day 0.048 0.084 0.570 0.569 1.049 0.890
## earlyacademicyear 0.113 0.080 1.416 0.157 1.120 0.957
## white -0.044 0.083 -0.528 0.598 0.957 0.814
## structuraletiology -0.020 0.098 -0.207 0.836 0.980 0.809
## priorepilepsy -0.018 0.096 -0.190 0.849 0.982 0.814
## status 0.323 0.100 3.234 0.001 1.382 1.136
## ageyears -0.005 0.008 -0.626 0.531 0.995 0.978
## SEXnumeric -0.002 0.083 -0.022 0.982 0.998 0.848
## upper .95
## intercept 19.078
## arm 1.240
## TYPESTATUSnumeric 1.411
## HOSPITALONSETnumeric 1.651
## day 1.237
## earlyacademicyear 1.310
## white 1.126
## structuraletiology 1.187
## priorepilepsy 1.184
## status 1.681
## ageyears 1.011
## SEXnumeric 1.174
# First BZD later than 60 minutes
CrossTable(pSERG$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 266 | 62 |
## | 0.811 | 0.189 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 237
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 190 | 47 |
## | 0.802 | 0.198 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 76 | 15 |
## | 0.835 | 0.165 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore60min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore60min and pSERG$awareness2017
## p-value = 0.5323
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3904113 1.5588798
## sample estimates:
## odds ratio
## 0.7984247
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=60,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.373 -6.350 3.603 0.589
## RMST (arm=1)/(arm=0) 0.954 0.774 1.177 0.662
## RMTL (arm=1)/(arm=0) 1.044 0.909 1.199 0.544
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 33.528 3.619 9.265 0.000 26.436 40.621
## arm -1.373 2.539 -0.541 0.589 -6.350 3.603
## TYPESTATUSnumeric -6.758 2.223 -3.040 0.002 -11.116 -2.400
## HOSPITALONSETnumeric -9.696 2.384 -4.067 0.000 -14.369 -5.023
## day -1.638 2.369 -0.692 0.489 -6.281 3.005
## earlyacademicyear -3.367 2.286 -1.473 0.141 -7.848 1.114
## white 0.464 2.414 0.192 0.847 -4.267 5.196
## structuraletiology 0.929 2.760 0.337 0.736 -4.480 6.339
## priorepilepsy 1.514 2.554 0.593 0.553 -3.491 6.520
## status -10.981 2.902 -3.784 0.000 -16.669 -5.293
## ageyears 0.141 0.229 0.617 0.537 -0.308 0.591
## SEXnumeric -0.695 2.352 -0.296 0.767 -5.305 3.914
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.544 0.142 24.898 0.000 34.617 26.189
## arm -0.047 0.107 -0.438 0.662 0.954 0.774
## TYPESTATUSnumeric -0.280 0.098 -2.850 0.004 0.756 0.624
## HOSPITALONSETnumeric -0.421 0.114 -3.696 0.000 0.656 0.525
## day -0.067 0.093 -0.722 0.470 0.935 0.779
## earlyacademicyear -0.131 0.093 -1.409 0.159 0.877 0.730
## white 0.013 0.097 0.137 0.891 1.013 0.838
## structuraletiology 0.040 0.109 0.368 0.713 1.041 0.841
## priorepilepsy 0.047 0.096 0.489 0.625 1.048 0.869
## status -0.509 0.154 -3.301 0.001 0.601 0.444
## ageyears 0.005 0.009 0.589 0.556 1.005 0.988
## SEXnumeric -0.023 0.093 -0.250 0.803 0.977 0.814
## upper .95
## intercept 45.757
## arm 1.177
## TYPESTATUSnumeric 0.916
## HOSPITALONSETnumeric 0.820
## day 1.122
## earlyacademicyear 1.053
## white 1.225
## structuraletiology 1.289
## priorepilepsy 1.264
## status 0.813
## ageyears 1.023
## SEXnumeric 1.173
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.302 0.107 30.768 0.000 27.155 22.005
## arm 0.043 0.071 0.607 0.544 1.044 0.909
## TYPESTATUSnumeric 0.191 0.062 3.089 0.002 1.210 1.072
## HOSPITALONSETnumeric 0.267 0.065 4.106 0.000 1.306 1.150
## day 0.047 0.070 0.676 0.499 1.048 0.914
## earlyacademicyear 0.099 0.066 1.505 0.132 1.104 0.971
## white -0.017 0.069 -0.242 0.809 0.983 0.859
## structuraletiology -0.026 0.080 -0.320 0.749 0.975 0.833
## priorepilepsy -0.051 0.078 -0.651 0.515 0.950 0.816
## status 0.297 0.078 3.799 0.000 1.346 1.155
## ageyears -0.004 0.007 -0.637 0.524 0.996 0.982
## SEXnumeric 0.021 0.068 0.314 0.754 1.022 0.894
## upper .95
## intercept 33.511
## arm 1.199
## TYPESTATUSnumeric 1.366
## HOSPITALONSETnumeric 1.483
## day 1.202
## earlyacademicyear 1.255
## white 1.126
## structuraletiology 1.140
## priorepilepsy 1.107
## status 1.569
## ageyears 1.009
## SEXnumeric 1.167
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 97 | 231 |
## | 0.296 | 0.704 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 237
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 68 | 169 |
## | 0.287 | 0.713 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 29 | 62 |
## | 0.319 | 0.681 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore40min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore40min and pSERG$awareness2017
## p-value = 0.5905
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4959606 1.5127514
## sample estimates:
## odds ratio
## 0.8606414
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=40,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.846 -1.158 2.850 0.408
## RMST (arm=1)/(arm=0) 1.025 0.968 1.086 0.394
## RMTL (arm=1)/(arm=0) 0.872 0.587 1.295 0.497
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.455 1.692 20.367 0.000 31.140 37.771
## arm 0.846 1.022 0.827 0.408 -1.158 2.850
## TYPESTATUSnumeric 0.090 0.999 0.090 0.928 -1.868 2.048
## HOSPITALONSETnumeric -6.667 1.232 -5.412 0.000 -9.082 -4.252
## day -1.494 0.981 -1.524 0.128 -3.417 0.428
## earlyacademicyear 1.039 0.941 1.105 0.269 -0.804 2.883
## white 1.771 1.037 1.708 0.088 -0.261 3.803
## structuraletiology -1.388 1.177 -1.179 0.238 -3.696 0.919
## priorepilepsy 1.704 0.995 1.713 0.087 -0.245 3.653
## status -0.783 1.194 -0.656 0.512 -3.124 1.557
## ageyears 0.166 0.100 1.667 0.096 -0.029 0.361
## SEXnumeric 0.892 1.030 0.866 0.386 -1.127 2.911
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.536 0.049 71.796 0.000 34.345 31.184
## arm 0.025 0.029 0.853 0.394 1.025 0.968
## TYPESTATUSnumeric 0.004 0.028 0.131 0.896 1.004 0.949
## HOSPITALONSETnumeric -0.197 0.039 -5.043 0.000 0.821 0.761
## day -0.042 0.028 -1.506 0.132 0.959 0.907
## earlyacademicyear 0.029 0.027 1.086 0.277 1.030 0.977
## white 0.051 0.030 1.673 0.094 1.052 0.991
## structuraletiology -0.040 0.035 -1.150 0.250 0.961 0.897
## priorepilepsy 0.048 0.028 1.692 0.091 1.049 0.992
## status -0.022 0.034 -0.635 0.525 0.979 0.916
## ageyears 0.005 0.003 1.676 0.094 1.005 0.999
## SEXnumeric 0.025 0.029 0.838 0.402 1.025 0.967
## upper .95
## intercept 37.826
## arm 1.086
## TYPESTATUSnumeric 1.061
## HOSPITALONSETnumeric 0.887
## day 1.013
## earlyacademicyear 1.086
## white 1.116
## structuraletiology 1.029
## priorepilepsy 1.108
## status 1.046
## ageyears 1.010
## SEXnumeric 1.086
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.508 0.341 4.420 0.000 4.519 2.315
## arm -0.137 0.202 -0.680 0.497 0.872 0.587
## TYPESTATUSnumeric 0.050 0.225 0.224 0.823 1.052 0.676
## HOSPITALONSETnumeric 1.252 0.215 5.813 0.000 3.496 2.293
## day 0.337 0.215 1.572 0.116 1.401 0.920
## earlyacademicyear -0.234 0.195 -1.197 0.231 0.792 0.540
## white -0.362 0.199 -1.823 0.068 0.696 0.472
## structuraletiology 0.266 0.204 1.303 0.192 1.304 0.875
## priorepilepsy -0.430 0.252 -1.708 0.088 0.650 0.397
## status 0.236 0.279 0.847 0.397 1.267 0.733
## ageyears -0.037 0.024 -1.513 0.130 0.964 0.919
## SEXnumeric -0.244 0.214 -1.137 0.255 0.784 0.515
## upper .95
## intercept 8.821
## arm 1.295
## TYPESTATUSnumeric 1.636
## HOSPITALONSETnumeric 5.332
## day 2.135
## earlyacademicyear 1.160
## white 1.028
## structuraletiology 1.944
## priorepilepsy 1.066
## status 2.188
## ageyears 1.011
## SEXnumeric 1.193
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 150 | 178 |
## | 0.457 | 0.543 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 237
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 107 | 130 |
## | 0.451 | 0.549 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 43 | 48 |
## | 0.473 | 0.527 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore60min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore60min and pSERG$awareness2017
## p-value = 0.8046
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5500938 1.5376924
## sample estimates:
## odds ratio
## 0.9190219
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=60,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.882 -2.818 4.581 0.640
## RMST (arm=1)/(arm=0) 1.021 0.944 1.104 0.607
## RMTL (arm=1)/(arm=0) 0.957 0.715 1.280 0.767
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 48.144 2.973 16.196 0.000 42.318 53.970
## arm 0.882 1.888 0.467 0.640 -2.818 4.581
## TYPESTATUSnumeric -2.317 1.835 -1.263 0.206 -5.913 1.278
## HOSPITALONSETnumeric -12.915 2.130 -6.063 0.000 -17.090 -8.740
## day -3.295 1.781 -1.849 0.064 -6.786 0.197
## earlyacademicyear 2.069 1.720 1.203 0.229 -1.303 5.440
## white 2.791 1.873 1.490 0.136 -0.880 6.462
## structuraletiology -3.130 2.086 -1.500 0.134 -7.219 0.959
## priorepilepsy 3.427 1.809 1.894 0.058 -0.119 6.974
## status -1.606 2.122 -0.757 0.449 -5.766 2.553
## ageyears 0.313 0.175 1.793 0.073 -0.029 0.656
## SEXnumeric 1.948 1.823 1.069 0.285 -1.625 5.520
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.869 0.064 60.472 0.000 47.880 42.238
## arm 0.021 0.040 0.514 0.607 1.021 0.944
## TYPESTATUSnumeric -0.047 0.039 -1.200 0.230 0.954 0.884
## HOSPITALONSETnumeric -0.285 0.052 -5.498 0.000 0.752 0.679
## day -0.068 0.037 -1.832 0.067 0.934 0.868
## earlyacademicyear 0.042 0.036 1.164 0.244 1.043 0.972
## white 0.058 0.041 1.426 0.154 1.060 0.979
## structuraletiology -0.067 0.047 -1.435 0.151 0.935 0.854
## priorepilepsy 0.070 0.038 1.855 0.064 1.072 0.996
## status -0.031 0.044 -0.711 0.477 0.969 0.888
## ageyears 0.006 0.004 1.798 0.072 1.006 0.999
## SEXnumeric 0.039 0.038 1.015 0.310 1.040 0.964
## upper .95
## intercept 54.277
## arm 1.104
## TYPESTATUSnumeric 1.030
## HOSPITALONSETnumeric 0.832
## day 1.005
## earlyacademicyear 1.120
## white 1.147
## structuraletiology 1.025
## priorepilepsy 1.154
## status 1.057
## ageyears 1.014
## SEXnumeric 1.121
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.366 0.238 9.947 0.000 10.655 6.684
## arm -0.044 0.149 -0.296 0.767 0.957 0.715
## TYPESTATUSnumeric 0.225 0.151 1.487 0.137 1.252 0.931
## HOSPITALONSETnumeric 0.975 0.151 6.469 0.000 2.651 1.973
## day 0.285 0.156 1.821 0.069 1.329 0.978
## earlyacademicyear -0.190 0.145 -1.314 0.189 0.827 0.623
## white -0.244 0.144 -1.699 0.089 0.783 0.591
## structuraletiology 0.248 0.148 1.669 0.095 1.281 0.958
## priorepilepsy -0.332 0.173 -1.923 0.054 0.717 0.511
## status 0.183 0.192 0.955 0.340 1.201 0.825
## ageyears -0.028 0.017 -1.683 0.092 0.972 0.941
## SEXnumeric -0.200 0.152 -1.318 0.188 0.819 0.608
## upper .95
## intercept 16.983
## arm 1.280
## TYPESTATUSnumeric 1.684
## HOSPITALONSETnumeric 3.562
## day 1.806
## earlyacademicyear 1.098
## white 1.038
## structuraletiology 1.713
## priorepilepsy 1.006
## status 1.749
## ageyears 1.005
## SEXnumeric 1.102
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 328
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 233 | 95 |
## | 0.710 | 0.290 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 237
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 166 | 71 |
## | 0.700 | 0.300 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 91
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 67 | 24 |
## | 0.736 | 0.264 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore120min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore120min and pSERG$awareness2017
## p-value = 0.5874
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4639769 1.4808285
## sample estimates:
## odds ratio
## 0.8379485
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=120,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 1.113 -7.569 9.794 0.802
## RMST (arm=1)/(arm=0) 1.021 0.904 1.154 0.736
## RMTL (arm=1)/(arm=0) 0.990 0.826 1.185 0.909
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 82.174 6.616 12.420 0.000 69.206 95.142
## arm 1.113 4.430 0.251 0.802 -7.569 9.794
## TYPESTATUSnumeric -18.974 4.069 -4.663 0.000 -26.950 -10.998
## HOSPITALONSETnumeric -28.567 4.545 -6.286 0.000 -37.474 -19.659
## day -7.881 4.132 -1.907 0.057 -15.980 0.219
## earlyacademicyear 0.921 4.052 0.227 0.820 -7.021 8.863
## white 4.342 4.279 1.015 0.310 -4.045 12.729
## structuraletiology -8.465 4.795 -1.765 0.078 -17.864 0.934
## priorepilepsy 6.921 4.330 1.598 0.110 -1.565 15.408
## status -6.584 5.079 -1.296 0.195 -16.539 3.372
## ageyears 0.808 0.388 2.083 0.037 0.048 1.568
## SEXnumeric 3.326 4.177 0.796 0.426 -4.861 11.514
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.409 0.093 47.196 0.000 82.211 68.455
## arm 0.021 0.062 0.337 0.736 1.021 0.904
## TYPESTATUSnumeric -0.273 0.062 -4.419 0.000 0.761 0.674
## HOSPITALONSETnumeric -0.427 0.076 -5.621 0.000 0.652 0.562
## day -0.109 0.057 -1.921 0.055 0.897 0.802
## earlyacademicyear 0.010 0.056 0.179 0.858 1.010 0.905
## white 0.054 0.062 0.873 0.383 1.055 0.935
## structuraletiology -0.120 0.073 -1.638 0.101 0.887 0.769
## priorepilepsy 0.090 0.058 1.537 0.124 1.094 0.976
## status -0.082 0.072 -1.144 0.252 0.921 0.800
## ageyears 0.010 0.005 2.048 0.041 1.010 1.000
## SEXnumeric 0.043 0.058 0.747 0.455 1.044 0.932
## upper .95
## intercept 98.731
## arm 1.154
## TYPESTATUSnumeric 0.859
## HOSPITALONSETnumeric 0.757
## day 1.002
## earlyacademicyear 1.128
## white 1.190
## structuraletiology 1.024
## priorepilepsy 1.226
## status 1.060
## ageyears 1.020
## SEXnumeric 1.170
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.632 0.141 25.828 0.000 37.798 28.692
## arm -0.011 0.092 -0.114 0.909 0.990 0.826
## TYPESTATUSnumeric 0.387 0.083 4.660 0.000 1.473 1.251
## HOSPITALONSETnumeric 0.564 0.089 6.310 0.000 1.758 1.475
## day 0.166 0.092 1.816 0.069 1.181 0.987
## earlyacademicyear -0.025 0.087 -0.291 0.771 0.975 0.821
## white -0.107 0.087 -1.231 0.218 0.898 0.757
## structuraletiology 0.176 0.092 1.905 0.057 1.192 0.995
## priorepilepsy -0.165 0.100 -1.641 0.101 0.848 0.696
## status 0.164 0.110 1.491 0.136 1.178 0.950
## ageyears -0.019 0.009 -2.032 0.042 0.981 0.963
## SEXnumeric -0.081 0.090 -0.902 0.367 0.922 0.774
## upper .95
## intercept 49.793
## arm 1.185
## TYPESTATUSnumeric 1.733
## HOSPITALONSETnumeric 2.094
## day 1.413
## earlyacademicyear 1.157
## white 1.066
## structuraletiology 1.429
## priorepilepsy 1.033
## status 1.462
## ageyears 0.999
## SEXnumeric 1.099
# First CI later than 60 minutes
CrossTable(pSERG$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 152
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 17 | 135 |
## | 0.112 | 0.888 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 115
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 11 | 104 |
## | 0.096 | 0.904 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 37
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 31 |
## | 0.162 | 0.838 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstCImore60min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore60min and pSERG$awareness2017
## p-value = 0.3665
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1691578 1.9595938
## sample estimates:
## odds ratio
## 0.5489383
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness2017, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.738 -6.312 2.835 0.456
## RMST (arm=1)/(arm=0) 0.970 0.895 1.052 0.463
## RMTL (arm=1)/(arm=0) 2.268 0.396 12.996 0.358
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 59.428 1.859 31.965 0.000 55.784 63.071
## arm -1.738 2.334 -0.745 0.456 -6.312 2.835
## TYPESTATUSnumeric 0.121 1.607 0.075 0.940 -3.030 3.271
## HOSPITALONSETnumeric -0.865 1.939 -0.446 0.656 -4.666 2.936
## day -2.789 1.239 -2.251 0.024 -5.218 -0.361
## earlyacademicyear -0.643 1.462 -0.440 0.660 -3.508 2.222
## white 1.161 1.566 0.741 0.459 -1.909 4.231
## structuraletiology -0.894 1.865 -0.479 0.632 -4.548 2.761
## priorepilepsy -0.396 1.289 -0.307 0.759 -2.922 2.131
## status 3.099 1.177 2.633 0.008 0.792 5.407
## ageyears -0.017 0.148 -0.118 0.906 -0.308 0.273
## SEXnumeric -0.198 1.596 -0.124 0.902 -3.326 2.931
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.085 0.032 126.425 0.000 59.413 55.768
## arm -0.030 0.041 -0.735 0.463 0.970 0.895
## TYPESTATUSnumeric 0.002 0.028 0.073 0.942 1.002 0.949
## HOSPITALONSETnumeric -0.015 0.034 -0.440 0.660 0.985 0.921
## day -0.048 0.022 -2.216 0.027 0.953 0.913
## earlyacademicyear -0.011 0.025 -0.447 0.655 0.989 0.941
## white 0.020 0.028 0.741 0.458 1.021 0.967
## structuraletiology -0.016 0.033 -0.471 0.637 0.985 0.923
## priorepilepsy -0.007 0.022 -0.303 0.762 0.993 0.951
## status 0.053 0.020 2.595 0.009 1.054 1.013
## ageyears 0.000 0.003 -0.112 0.911 1.000 0.995
## SEXnumeric -0.004 0.028 -0.133 0.894 0.996 0.944
## upper .95
## intercept 63.297
## arm 1.052
## TYPESTATUSnumeric 1.058
## HOSPITALONSETnumeric 1.053
## day 0.994
## earlyacademicyear 1.039
## white 1.077
## structuraletiology 1.050
## priorepilepsy 1.038
## status 1.097
## ageyears 1.005
## SEXnumeric 1.052
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept -0.700 1.118 -0.626 0.531 0.497 0.056
## arm 0.819 0.891 0.919 0.358 2.268 0.396
## TYPESTATUSnumeric -0.114 0.692 -0.165 0.869 0.892 0.230
## HOSPITALONSETnumeric 0.395 0.642 0.615 0.538 1.485 0.422
## day 1.630 0.873 1.867 0.062 5.106 0.922
## earlyacademicyear 0.229 0.640 0.358 0.721 1.257 0.358
## white -0.276 0.577 -0.478 0.633 0.759 0.245
## structuraletiology 0.451 0.558 0.809 0.419 1.570 0.526
## priorepilepsy 0.228 0.579 0.394 0.694 1.256 0.404
## status -18.018 0.607 -29.677 0.000 0.000 0.000
## ageyears 0.020 0.050 0.400 0.689 1.020 0.924
## SEXnumeric -0.139 0.720 -0.193 0.847 0.870 0.212
## upper .95
## intercept 4.440
## arm 12.996
## TYPESTATUSnumeric 3.465
## HOSPITALONSETnumeric 5.226
## day 28.285
## earlyacademicyear 4.411
## white 2.353
## structuraletiology 4.687
## priorepilepsy 3.905
## status 0.000
## ageyears 1.126
## SEXnumeric 3.566
# First CI later than 120 minutes
CrossTable(pSERG$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 152
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 45 | 107 |
## | 0.296 | 0.704 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 115
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 29 | 86 |
## | 0.252 | 0.748 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 37
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 16 | 21 |
## | 0.432 | 0.568 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstCImore120min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore120min and pSERG$awareness2017
## p-value = 0.04126
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1911348 1.0429817
## sample estimates:
## odds ratio
## 0.4451879
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness2017, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -10.329 -22.681 2.024 0.101
## RMST (arm=1)/(arm=0) 0.905 0.801 1.024 0.112
## RMTL (arm=1)/(arm=0) 1.973 0.949 4.106 0.069
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 112.093 6.571 17.060 0.000 99.215 124.972
## arm -10.329 6.302 -1.639 0.101 -22.681 2.024
## TYPESTATUSnumeric -1.920 4.992 -0.385 0.700 -11.704 7.863
## HOSPITALONSETnumeric 1.181 5.276 0.224 0.823 -9.160 11.523
## day -7.005 4.282 -1.636 0.102 -15.398 1.387
## earlyacademicyear -3.480 4.663 -0.746 0.456 -12.619 5.660
## white 3.305 4.820 0.686 0.493 -6.142 12.752
## structuraletiology -3.885 5.737 -0.677 0.498 -15.129 7.360
## priorepilepsy -4.959 4.781 -1.037 0.300 -14.329 4.411
## status 9.618 4.539 2.119 0.034 0.723 18.514
## ageyears 0.270 0.412 0.656 0.512 -0.537 1.078
## SEXnumeric -0.866 4.698 -0.184 0.854 -10.074 8.342
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.719 0.061 77.068 0.000 112.075 99.400
## arm -0.100 0.063 -1.587 0.112 0.905 0.801
## TYPESTATUSnumeric -0.018 0.048 -0.368 0.713 0.983 0.895
## HOSPITALONSETnumeric 0.012 0.050 0.234 0.815 1.012 0.917
## day -0.066 0.041 -1.623 0.105 0.936 0.864
## earlyacademicyear -0.033 0.044 -0.743 0.458 0.968 0.888
## white 0.031 0.046 0.677 0.498 1.032 0.943
## structuraletiology -0.037 0.055 -0.668 0.504 0.964 0.864
## priorepilepsy -0.046 0.045 -1.024 0.306 0.955 0.873
## status 0.090 0.043 2.104 0.035 1.094 1.006
## ageyears 0.003 0.004 0.658 0.511 1.003 0.995
## SEXnumeric -0.009 0.044 -0.195 0.845 0.991 0.909
## upper .95
## intercept 126.366
## arm 1.024
## TYPESTATUSnumeric 1.079
## HOSPITALONSETnumeric 1.116
## day 1.014
## earlyacademicyear 1.055
## white 1.129
## structuraletiology 1.074
## priorepilepsy 1.043
## status 1.189
## ageyears 1.010
## SEXnumeric 1.081
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.092 0.541 3.868 0.000 8.101 2.806
## arm 0.680 0.374 1.819 0.069 1.973 0.949
## TYPESTATUSnumeric 0.176 0.336 0.525 0.600 1.193 0.617
## HOSPITALONSETnumeric -0.058 0.359 -0.162 0.871 0.943 0.467
## day 0.520 0.326 1.595 0.111 1.681 0.888
## earlyacademicyear 0.269 0.359 0.751 0.453 1.309 0.648
## white -0.251 0.344 -0.730 0.466 0.778 0.396
## structuraletiology 0.266 0.373 0.714 0.475 1.305 0.629
## priorepilepsy 0.382 0.343 1.115 0.265 1.465 0.749
## status -0.804 0.414 -1.941 0.052 0.448 0.199
## ageyears -0.019 0.031 -0.634 0.526 0.981 0.924
## SEXnumeric 0.029 0.360 0.079 0.937 1.029 0.508
## upper .95
## intercept 23.383
## arm 4.106
## TYPESTATUSnumeric 2.305
## HOSPITALONSETnumeric 1.907
## day 3.184
## earlyacademicyear 2.645
## white 1.527
## structuraletiology 2.710
## priorepilepsy 2.867
## status 1.008
## ageyears 1.041
## SEXnumeric 2.082
# First CI later than 240 minutes
CrossTable(pSERG$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 152
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 93 | 59 |
## | 0.612 | 0.388 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 115
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 70 | 45 |
## | 0.609 | 0.391 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 37
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 23 | 14 |
## | 0.622 | 0.378 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstCImore240min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore240min and pSERG$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4054674 2.1555749
## sample estimates:
## odds ratio
## 0.9471852
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness2017, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -15.759 -46.597 15.080 0.317
## RMST (arm=1)/(arm=0) 0.908 0.746 1.105 0.337
## RMTL (arm=1)/(arm=0) 1.234 0.844 1.804 0.278
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 192.735 19.144 10.068 0.000 155.214 230.257
## arm -15.759 15.734 -1.002 0.317 -46.597 15.080
## TYPESTATUSnumeric -23.895 12.929 -1.848 0.065 -49.235 1.445
## HOSPITALONSETnumeric -2.718 13.306 -0.204 0.838 -28.798 23.362
## day -14.779 12.141 -1.217 0.224 -38.574 9.017
## earlyacademicyear -8.959 12.423 -0.721 0.471 -33.307 15.390
## white 6.239 13.094 0.476 0.634 -19.425 31.903
## structuraletiology -4.503 15.098 -0.298 0.766 -34.095 25.089
## priorepilepsy -6.358 13.866 -0.459 0.647 -33.534 20.819
## status 5.165 14.247 0.363 0.717 -22.758 33.088
## ageyears 0.275 1.109 0.248 0.804 -1.899 2.449
## SEXnumeric -6.738 12.135 -0.555 0.579 -30.522 17.046
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.268 0.111 47.579 0.000 193.933 156.104
## arm -0.096 0.100 -0.961 0.337 0.908 0.746
## TYPESTATUSnumeric -0.148 0.083 -1.782 0.075 0.863 0.733
## HOSPITALONSETnumeric -0.015 0.081 -0.191 0.848 0.985 0.841
## day -0.086 0.071 -1.207 0.227 0.917 0.798
## earlyacademicyear -0.052 0.074 -0.700 0.484 0.950 0.822
## white 0.036 0.078 0.458 0.647 1.037 0.889
## structuraletiology -0.028 0.091 -0.311 0.756 0.972 0.814
## priorepilepsy -0.036 0.081 -0.441 0.659 0.965 0.823
## status 0.032 0.084 0.379 0.705 1.032 0.875
## ageyears 0.001 0.006 0.227 0.820 1.001 0.989
## SEXnumeric -0.040 0.072 -0.559 0.576 0.961 0.834
## upper .95
## intercept 240.929
## arm 1.105
## TYPESTATUSnumeric 1.015
## HOSPITALONSETnumeric 1.153
## day 1.055
## earlyacademicyear 1.097
## white 1.209
## structuraletiology 1.162
## priorepilepsy 1.131
## status 1.218
## ageyears 1.014
## SEXnumeric 1.106
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.910 0.287 13.615 0.000 49.884 28.414
## arm 0.210 0.194 1.084 0.278 1.234 0.844
## TYPESTATUSnumeric 0.312 0.161 1.931 0.053 1.366 0.995
## HOSPITALONSETnumeric 0.041 0.178 0.233 0.816 1.042 0.735
## day 0.218 0.179 1.218 0.223 1.243 0.876
## earlyacademicyear 0.135 0.177 0.763 0.445 1.145 0.809
## white -0.095 0.183 -0.518 0.604 0.910 0.635
## structuraletiology 0.054 0.207 0.263 0.793 1.056 0.704
## priorepilepsy 0.099 0.200 0.496 0.620 1.104 0.746
## status -0.065 0.203 -0.321 0.748 0.937 0.630
## ageyears -0.005 0.016 -0.296 0.767 0.995 0.964
## SEXnumeric 0.093 0.172 0.542 0.588 1.098 0.783
## upper .95
## intercept 87.579
## arm 1.804
## TYPESTATUSnumeric 1.874
## HOSPITALONSETnumeric 1.478
## day 1.766
## earlyacademicyear 1.619
## white 1.302
## structuraletiology 1.583
## priorepilepsy 1.634
## status 1.394
## ageyears 1.027
## SEXnumeric 1.538
## OUT OF THE HOSPITAL
# At least one benzodiazepine before hospital arrival
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 157
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 59 | 98 |
## | 0.376 | 0.624 |
## |-----------|-----------|
##
##
##
##
# At least one benzodiazepine before hospital arrival depending on awareness
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 126
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 51 | 75 |
## | 0.405 | 0.595 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 31
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 8 | 23 |
## | 0.258 | 0.742 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017)
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$AEDbeforehospital and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.1513
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.7657267 5.4431247
## sample estimates:
## odds ratio
## 1.94711
# Logistic regression adjusting for potential confounders
logistic_out_of_hospital_BZD <- glm(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX, family="binomial")
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD), confint(logistic_out_of_hospital_BZD, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD))[ , 4])
## Waiting for profiling to be done...
## Odds ratio
## (Intercept) 3.4107042
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 2.6032630
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.3198674
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.0773025
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.2033688
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.7238517
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.6376308
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0962135
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 6.9722164
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0323259
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.5535459
## 2.5 %
## (Intercept) 0.9737275
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.9791029
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.1283232
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.5044118
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.5799497
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.3252843
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.2672026
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.5252690
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.1232997
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9628699
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.2581508
## 97.5 %
## (Intercept) 12.9228368
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 7.5078220
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.7436255
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 2.3080334
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.5115588
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.5756203
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.5114287
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 2.2894750
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 32.0796254
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.1095969
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.1576292
## p-value
## (Intercept) 0.061170161
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.063202056
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.010407453
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.847133469
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.619126232
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.419595091
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.305559108
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.806037721
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.003866216
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.375832055
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.120832720
# At least one benzodiazepine before hospital arrival among those with prior epilepsy
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 85
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 29 | 56 |
## | 0.341 | 0.659 |
## |-----------|-----------|
##
##
##
##
# At least one benzodiazepine before hospital arrival depending on awareness
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness2017 == 0), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 67
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 39 |
## | 0.418 | 0.582 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness2017 == 1), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 1 | 17 |
## | 0.056 | 0.944 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$awareness2017)
##
## Fisher's Exact Test for Count Data
##
## data:
## p-value = 0.004169
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.677579 526.609412
## sample estimates:
## odds ratio
## 11.95204
# Logistic regression adjusting for potential confounders among those with prior epilepsy
logistic_out_of_hospital_BZD_prior_epilepsy <- glm(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital ~ pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$day + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$white +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$structuraletiology +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$status + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$SEX, family="binomial")
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD_prior_epilepsy), confint(logistic_out_of_hospital_BZD_prior_epilepsy, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD_prior_epilepsy))[ , 4])
## Waiting for profiling to be done...
## Odds ratio
## (Intercept) 1.4942690
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness2017 21.9410103
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.3826524
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 1.9304558
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.7720178
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.5789567
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 1.1847008
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 9.2745040
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 1.1110673
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.5327827
## 2.5 %
## (Intercept) 0.19803326
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness2017 3.18941042
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.09695779
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 0.62686806
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.23331676
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.16834295
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 0.31837716
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 1.87152844
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 0.98483043
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.16140764
## 97.5 %
## (Intercept) 12.343563
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness2017 459.883730
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 1.362955
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 6.286297
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 2.490484
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 1.882913
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 4.583590
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 73.927343
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 1.272520
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 1.633239
## p-value
## (Intercept) 0.698426134
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness2017 0.008139511
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.148696991
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 0.258434442
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.665081969
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.368944308
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 0.801018438
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 0.013883210
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 0.101668884
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.280933038
# Patients in each group
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 168 | 54 |
## | 0.757 | 0.243 |
## |-----------|-----------|
##
##
##
##
# Time to first BZD
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 7.00 20.00 68.93 55.00 1264.00
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0)
## [1] 153.5504
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 222 222 20 20 30
# Figure time to first BZD
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 7.75 20.00 70.96 56.25 1264.00
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 20.00 62.59 48.75 517.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=0 168 87.0 89.2
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=1 54 29.4 27.2
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=0 0.0546 0.367
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=1 0.1793 0.367
##
## Chisq= 0.4 on 1 degrees of freedom, p= 0.5
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.5443757
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 222, number of events= 222
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.004282
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.435545
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.060566
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.129393
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.112752
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.166863
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.049098
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.614537
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.000615
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.146140
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.004292
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.646912
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.062438
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.138138
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.119355
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.181593
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.050323
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.848800
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.999385
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.157358
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.164358
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.156263
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.144272
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.138468
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.150598
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.173591
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.147696
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.194934
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.014304
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.141588
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.026
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -2.787
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.420
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.934
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.749
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.961
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.332
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 3.153
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.043
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.032
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.97921
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.00532 **
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.67463
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.35006
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.45404
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.33643
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.73957
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.00162 **
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.96570
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.30200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.0043
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6469
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.0624
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.1381
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.1194
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.1816
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0503
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.8488
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9994
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.1574
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.9957
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.5458
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.9412
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8786
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.8934
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8463
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.9521
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.5409
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0006
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8640
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.7277
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.4762
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8008
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8676
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.8333
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8408
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.7863
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.2617
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9718
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8769
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.3860
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.8787
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.4096
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.4930
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.5037
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.6605
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.4029
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.7091
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0278
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.5275
##
## Concordance= 0.599 (se = 0.025 )
## Rsquare= 0.102 (max possible= 1 )
## Likelihood ratio test= 23.78 on 10 df, p=0.008
## Wald test = 25.49 on 10 df, p=0.004
## Score (logrank) test = 26.45 on 10 df, p=0.003
# Time to first non-BZD AED
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 45.5 81.0 192.7 170.0 4320.0
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0)
## [1] 375.2449
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 222 222 81 70 103
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 45.0 83.0 203.2 165.2 4320.0
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 51.25 78.50 160.02 206.75 720.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=0 168 85.7 84.5
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=1 54 26.7 27.9
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=0 0.0173 0.105
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=1 0.0522 0.105
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.7462884
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 222, number of events= 222
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.010312
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.761135
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.152477
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.053240
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.086916
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology -0.007509
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy -0.133687
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.113238
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.021746
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.238823
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.989741
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.467136
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.164716
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.054683
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.916754
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.992519
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.874864
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.119899
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.978489
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.269753
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.166239
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.154171
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.147347
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.140347
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.146723
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.170864
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.150690
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.193886
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.014084
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.147449
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.062
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -4.937
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.035
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.379
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.592
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology -0.044
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy -0.887
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.584
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -1.544
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.620
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.951
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 7.94e-07 ***
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.301
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.704
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.554
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.965
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.375
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.559
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.123
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.9897
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.4671
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.1647
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.0547
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.9168
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.9925
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.8749
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.1199
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9785
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.2698
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.0104
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 2.1407
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8586
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.9482
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.0908
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.0075
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.1430
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.8929
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0220
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7876
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.7145
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.3453
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8726
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8010
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.6876
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.7101
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.6511
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.7658
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9518
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.9511
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.3710
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6319
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.5547
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.3886
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.2222
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.3873
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.1755
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.6376
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0059
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.6952
##
## Concordance= 0.621 (se = 0.023 )
## Rsquare= 0.153 (max possible= 1 )
## Likelihood ratio test= 36.79 on 10 df, p=6e-05
## Wald test = 37.33 on 10 df, p=5e-05
## Score (logrank) test = 39.01 on 10 df, p=3e-05
# Time to first CI
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 15.0 118.0 172.0 506.4 626.0 4320.0 121
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## 1)
##
## 121 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 101 101 172 150 295
# Figure time to first CI
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20.0 123.5 172.0 474.7 604.0 4320.0 85
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 15.0 67.5 217.5 652.4 900.0 3008.0 36
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017, rho = 1)
##
## n=101, 121 observations deleted due to missingness.
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=0 83 41.61 42.48
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=1 18 9.53 8.67
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=0 0.0175 0.162
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017=1 0.0855 0.162
##
## Chisq= 0.2 on 1 degrees of freedom, p= 0.7
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.6874723
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 101, number of events= 101
## (121 observations deleted due to missingness)
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.1311061
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.2350549
## pSERG[pSERG$HOSPITALONSET == "no", ]$day -0.0735340
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.3557352
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.3903055
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.3745878
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.1919138
## pSERG[pSERG$HOSPITALONSET == "no", ]$status -0.0097923
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.0007926
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.1934605
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.8771247
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.7905274
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.9291046
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.4272296
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.6768500
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.4543919
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.2115661
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.9902555
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9992078
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.2134415
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.2886248
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.2355082
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.2250262
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.2340959
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.2620340
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.2815969
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.2562268
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.2775334
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.0218656
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.2370470
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.454
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.998
## pSERG[pSERG$HOSPITALONSET == "no", ]$day -0.327
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.520
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -1.490
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.330
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.749
## pSERG[pSERG$HOSPITALONSET == "no", ]$status -0.035
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.036
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.816
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.650
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.318
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.744
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.129
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.136
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.183
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.454
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.972
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.971
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.414
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.8771
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.7905
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.9291
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.4272
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.6769
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.4544
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.2116
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.9903
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9992
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.2134
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.1401
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.2650
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.0763
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.7007
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.4774
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.6876
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.8254
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.0098
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0008
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8241
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.4982
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.4983
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.5978
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.9021
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.4050
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8375
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.7332
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.5748
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9573
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7625
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.544
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.254
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.444
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.258
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.131
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 2.526
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 2.002
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.706
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.043
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.931
##
## Concordance= 0.576 (se = 0.034 )
## Rsquare= 0.075 (max possible= 0.999 )
## Likelihood ratio test= 7.82 on 10 df, p=0.6
## Wald test = 7.99 on 10 df, p=0.6
## Score (logrank) test = 8.05 on 10 df, p=0.6
#### Recommendations and outliers out of the hospital
# First BZD later than 20 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 115 | 107 |
## | 0.518 | 0.482 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 168
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 87 | 81 |
## | 0.518 | 0.482 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 54
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 26 |
## | 0.519 | 0.481 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore20min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore20min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5145226 1.9287505
## sample estimates:
## odds ratio
## 0.9973663
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=20,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.264 -2.432 1.905 0.811
## RMST (arm=1)/(arm=0) 0.980 0.840 1.143 0.795
## RMTL (arm=1)/(arm=0) 1.035 0.712 1.504 0.858
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 17.999 1.534 11.730 0.000 14.991 21.006
## arm -0.264 1.106 -0.239 0.811 -2.432 1.905
## TYPESTATUSnumeric -0.104 0.919 -0.113 0.910 -1.905 1.698
## day -0.786 0.909 -0.865 0.387 -2.567 0.995
## earlyacademicyear -0.443 0.891 -0.498 0.619 -2.190 1.303
## white -0.663 0.922 -0.719 0.472 -2.470 1.145
## structuraletiology -0.952 1.091 -0.873 0.383 -3.089 1.186
## priorepilepsy -2.225 0.936 -2.379 0.017 -4.059 -0.392
## status -4.771 1.387 -3.440 0.001 -7.489 -2.053
## ageyears -0.036 0.093 -0.383 0.701 -0.218 0.146
## SEXnumeric 0.090 0.919 0.098 0.922 -1.712 1.892
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.910 0.103 28.345 0.000 18.349 15.005
## arm -0.020 0.079 -0.260 0.795 0.980 0.840
## TYPESTATUSnumeric -0.009 0.063 -0.136 0.892 0.991 0.876
## day -0.055 0.063 -0.879 0.379 0.946 0.837
## earlyacademicyear -0.030 0.062 -0.494 0.621 0.970 0.860
## white -0.045 0.063 -0.709 0.478 0.956 0.846
## structuraletiology -0.065 0.077 -0.851 0.395 0.937 0.806
## priorepilepsy -0.152 0.066 -2.310 0.021 0.859 0.756
## status -0.386 0.129 -3.004 0.003 0.680 0.528
## ageyears -0.003 0.006 -0.415 0.678 0.997 0.985
## SEXnumeric 0.007 0.064 0.115 0.909 1.007 0.889
## upper .95
## intercept 22.438
## arm 1.143
## TYPESTATUSnumeric 1.122
## day 1.070
## earlyacademicyear 1.094
## white 1.082
## structuraletiology 1.089
## priorepilepsy 0.977
## status 0.874
## ageyears 1.010
## SEXnumeric 1.142
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.008 0.320 3.151 0.002 2.741 1.464
## arm 0.034 0.191 0.179 0.858 1.035 0.712
## TYPESTATUSnumeric 0.010 0.171 0.058 0.954 1.010 0.723
## day 0.141 0.167 0.840 0.401 1.151 0.829
## earlyacademicyear 0.084 0.166 0.504 0.614 1.087 0.785
## white 0.128 0.176 0.729 0.466 1.137 0.806
## structuraletiology 0.175 0.190 0.920 0.357 1.191 0.820
## priorepilepsy 0.438 0.184 2.384 0.017 1.550 1.081
## status 0.668 0.181 3.692 0.000 1.950 1.368
## ageyears 0.005 0.017 0.297 0.766 1.005 0.972
## SEXnumeric -0.009 0.170 -0.053 0.958 0.991 0.711
## upper .95
## intercept 5.132
## arm 1.504
## TYPESTATUSnumeric 1.411
## day 1.597
## earlyacademicyear 1.506
## white 1.603
## structuraletiology 1.730
## priorepilepsy 2.223
## status 2.779
## ageyears 1.039
## SEXnumeric 1.382
# First BZD later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 151 | 71 |
## | 0.680 | 0.320 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 168
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 113 | 55 |
## | 0.673 | 0.327 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 54
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 38 | 16 |
## | 0.704 | 0.296 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore40min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore40min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.7389
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4129322 1.7553289
## sample estimates:
## odds ratio
## 0.8656447
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.283 -4.973 4.408 0.906
## RMST (arm=1)/(arm=0) 0.988 0.798 1.223 0.911
## RMTL (arm=1)/(arm=0) 1.018 0.783 1.323 0.895
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 27.875 3.120 8.935 0.000 21.760 33.989
## arm -0.283 2.393 -0.118 0.906 -4.973 4.408
## TYPESTATUSnumeric -3.300 1.952 -1.690 0.091 -7.126 0.526
## day -1.190 1.986 -0.599 0.549 -5.083 2.703
## earlyacademicyear -1.276 1.929 -0.661 0.508 -5.056 2.505
## white -0.018 2.006 -0.009 0.993 -3.949 3.912
## structuraletiology 0.345 2.404 0.144 0.886 -4.366 5.057
## priorepilepsy -1.493 2.032 -0.735 0.463 -5.475 2.489
## status -9.029 2.749 -3.284 0.001 -14.417 -3.640
## ageyears -0.074 0.193 -0.382 0.703 -0.451 0.304
## SEXnumeric -0.223 2.019 -0.110 0.912 -4.181 3.735
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.344 0.134 24.892 0.000 28.319 21.764
## arm -0.012 0.109 -0.112 0.911 0.988 0.798
## TYPESTATUSnumeric -0.150 0.090 -1.673 0.094 0.860 0.721
## day -0.054 0.087 -0.620 0.535 0.947 0.798
## earlyacademicyear -0.054 0.086 -0.629 0.530 0.948 0.801
## white 0.001 0.089 0.011 0.992 1.001 0.840
## structuraletiology 0.018 0.104 0.170 0.865 1.018 0.830
## priorepilepsy -0.069 0.089 -0.776 0.438 0.933 0.784
## status -0.488 0.173 -2.814 0.005 0.614 0.437
## ageyears -0.003 0.009 -0.383 0.702 0.997 0.980
## SEXnumeric -0.006 0.090 -0.067 0.947 0.994 0.834
## upper .95
## intercept 36.847
## arm 1.223
## TYPESTATUSnumeric 1.026
## day 1.124
## earlyacademicyear 1.121
## white 1.192
## structuraletiology 1.248
## priorepilepsy 1.111
## status 0.862
## ageyears 1.014
## SEXnumeric 1.185
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.538 0.190 13.323 0.000 12.655 8.712
## arm 0.018 0.134 0.132 0.895 1.018 0.783
## TYPESTATUSnumeric 0.185 0.111 1.676 0.094 1.204 0.969
## day 0.067 0.116 0.574 0.566 1.069 0.851
## earlyacademicyear 0.078 0.112 0.697 0.486 1.081 0.869
## white 0.004 0.115 0.032 0.974 1.004 0.801
## structuraletiology -0.016 0.143 -0.114 0.909 0.984 0.744
## priorepilepsy 0.082 0.121 0.680 0.496 1.086 0.857
## status 0.438 0.128 3.423 0.001 1.549 1.206
## ageyears 0.004 0.011 0.378 0.706 1.004 0.982
## SEXnumeric 0.019 0.117 0.162 0.871 1.019 0.810
## upper .95
## intercept 18.383
## arm 1.323
## TYPESTATUSnumeric 1.495
## day 1.343
## earlyacademicyear 1.345
## white 1.257
## structuraletiology 1.302
## priorepilepsy 1.375
## status 1.991
## ageyears 1.026
## SEXnumeric 1.282
# First BZD later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 175 | 47 |
## | 0.788 | 0.212 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 168
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 131 | 37 |
## | 0.780 | 0.220 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 54
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 44 | 10 |
## | 0.815 | 0.185 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.7028
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3292996 1.8258519
## sample estimates:
## odds ratio
## 0.8054587
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.875 -7.634 5.884 0.800
## RMST (arm=1)/(arm=0) 0.971 0.756 1.247 0.815
## RMTL (arm=1)/(arm=0) 1.029 0.838 1.264 0.786
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 35.863 4.648 7.716 0.000 26.753 44.973
## arm -0.875 3.448 -0.254 0.800 -7.634 5.884
## TYPESTATUSnumeric -6.354 2.854 -2.226 0.026 -11.948 -0.760
## day -2.103 2.950 -0.713 0.476 -7.884 3.679
## earlyacademicyear -2.147 2.859 -0.751 0.453 -7.750 3.456
## white -0.393 2.996 -0.131 0.895 -6.265 5.478
## structuraletiology 0.939 3.544 0.265 0.791 -6.007 7.886
## priorepilepsy 0.642 3.019 0.213 0.832 -5.275 6.558
## status -13.704 3.668 -3.736 0.000 -20.893 -6.515
## ageyears -0.085 0.280 -0.303 0.762 -0.634 0.464
## SEXnumeric -0.998 2.990 -0.334 0.739 -6.858 4.863
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.598 0.160 22.436 0.000 36.508 26.663
## arm -0.030 0.128 -0.234 0.815 0.971 0.756
## TYPESTATUSnumeric -0.235 0.109 -2.156 0.031 0.791 0.639
## day -0.075 0.104 -0.724 0.469 0.928 0.757
## earlyacademicyear -0.071 0.102 -0.693 0.488 0.932 0.763
## white -0.012 0.107 -0.110 0.912 0.988 0.801
## structuraletiology 0.037 0.122 0.301 0.763 1.037 0.817
## priorepilepsy 0.016 0.104 0.152 0.879 1.016 0.828
## status -0.612 0.196 -3.122 0.002 0.542 0.369
## ageyears -0.003 0.010 -0.308 0.758 0.997 0.978
## SEXnumeric -0.028 0.106 -0.264 0.791 0.972 0.790
## upper .95
## intercept 49.989
## arm 1.247
## TYPESTATUSnumeric 0.979
## day 1.136
## earlyacademicyear 1.138
## white 1.220
## structuraletiology 1.317
## priorepilepsy 1.247
## status 0.796
## ageyears 1.016
## SEXnumeric 1.197
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.206 0.154 20.883 0.000 24.685 18.270
## arm 0.028 0.105 0.271 0.786 1.029 0.838
## TYPESTATUSnumeric 0.196 0.088 2.224 0.026 1.217 1.024
## day 0.067 0.095 0.705 0.481 1.069 0.888
## earlyacademicyear 0.072 0.090 0.795 0.427 1.074 0.900
## white 0.014 0.094 0.147 0.884 1.014 0.844
## structuraletiology -0.027 0.115 -0.238 0.812 0.973 0.776
## priorepilepsy -0.025 0.098 -0.258 0.796 0.975 0.805
## status 0.379 0.099 3.811 0.000 1.461 1.202
## ageyears 0.003 0.009 0.294 0.769 1.003 0.985
## SEXnumeric 0.037 0.095 0.387 0.699 1.037 0.861
## upper .95
## intercept 33.351
## arm 1.264
## TYPESTATUSnumeric 1.447
## day 1.287
## earlyacademicyear 1.282
## white 1.218
## structuraletiology 1.220
## priorepilepsy 1.181
## status 1.776
## ageyears 1.020
## SEXnumeric 1.250
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 44 | 178 |
## | 0.198 | 0.802 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 168
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 35 | 133 |
## | 0.208 | 0.792 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 54
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 45 |
## | 0.167 | 0.833 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore40min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore40min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.5618
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5632003 3.3548033
## sample estimates:
## odds ratio
## 1.314194
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.896 -1.137 2.930 0.388
## RMST (arm=1)/(arm=0) 1.024 0.970 1.081 0.389
## RMTL (arm=1)/(arm=0) 0.681 0.279 1.663 0.399
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.702 1.819 19.073 0.000 31.136 38.268
## arm 0.896 1.038 0.864 0.388 -1.137 2.930
## TYPESTATUSnumeric -1.286 0.950 -1.353 0.176 -3.148 0.576
## day -0.548 0.996 -0.550 0.582 -2.499 1.404
## earlyacademicyear 1.327 0.918 1.446 0.148 -0.472 3.126
## white 0.959 1.067 0.899 0.368 -1.131 3.050
## structuraletiology -0.201 1.292 -0.156 0.876 -2.733 2.331
## priorepilepsy 1.878 1.040 1.805 0.071 -0.161 3.917
## status 0.836 1.000 0.836 0.403 -1.125 2.797
## ageyears 0.026 0.102 0.251 0.802 -0.174 0.226
## SEXnumeric 1.225 1.052 1.165 0.244 -0.836 3.287
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.548 0.050 70.727 0.000 34.745 31.491
## arm 0.024 0.028 0.861 0.389 1.024 0.970
## TYPESTATUSnumeric -0.034 0.026 -1.330 0.184 0.966 0.918
## day -0.015 0.027 -0.552 0.581 0.985 0.935
## earlyacademicyear 0.035 0.025 1.429 0.153 1.036 0.987
## white 0.026 0.029 0.896 0.370 1.026 0.970
## structuraletiology -0.005 0.035 -0.148 0.882 0.995 0.929
## priorepilepsy 0.051 0.028 1.799 0.072 1.052 0.995
## status 0.022 0.026 0.827 0.408 1.022 0.971
## ageyears 0.001 0.003 0.259 0.796 1.001 0.995
## SEXnumeric 0.033 0.028 1.151 0.250 1.033 0.977
## upper .95
## intercept 38.335
## arm 1.081
## TYPESTATUSnumeric 1.016
## day 1.038
## earlyacademicyear 1.088
## white 1.086
## structuraletiology 1.066
## priorepilepsy 1.111
## status 1.076
## ageyears 1.006
## SEXnumeric 1.093
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.750 0.508 3.445 0.001 5.752 2.126
## arm -0.385 0.456 -0.844 0.399 0.681 0.279
## TYPESTATUSnumeric 0.500 0.326 1.537 0.124 1.649 0.871
## day 0.191 0.372 0.514 0.607 1.211 0.584
## earlyacademicyear -0.556 0.364 -1.526 0.127 0.573 0.281
## white -0.318 0.358 -0.890 0.373 0.727 0.361
## structuraletiology 0.092 0.413 0.223 0.824 1.096 0.488
## priorepilepsy -0.714 0.447 -1.597 0.110 0.490 0.204
## status -0.424 0.543 -0.782 0.434 0.654 0.226
## ageyears -0.005 0.041 -0.111 0.911 0.995 0.919
## SEXnumeric -0.481 0.373 -1.289 0.197 0.618 0.298
## upper .95
## intercept 15.564
## arm 1.663
## TYPESTATUSnumeric 3.122
## day 2.509
## earlyacademicyear 1.171
## white 1.466
## structuraletiology 2.461
## priorepilepsy 1.176
## status 1.895
## ageyears 1.079
## SEXnumeric 1.284
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 80 | 142 |
## | 0.360 | 0.640 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 168
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 62 | 106 |
## | 0.369 | 0.631 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 54
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 18 | 36 |
## | 0.333 | 0.667 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.7449
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5881457 2.3831406
## sample estimates:
## odds ratio
## 1.168994
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 1.471 -2.549 5.490 0.473
## RMST (arm=1)/(arm=0) 1.028 0.952 1.110 0.479
## RMTL (arm=1)/(arm=0) 0.811 0.469 1.402 0.454
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 47.437 3.304 14.359 0.000 40.962 53.912
## arm 1.471 2.051 0.717 0.473 -2.549 5.490
## TYPESTATUSnumeric -4.400 1.904 -2.312 0.021 -8.131 -0.669
## day -1.362 1.895 -0.719 0.472 -5.076 2.352
## earlyacademicyear 2.154 1.816 1.186 0.236 -1.406 5.715
## white 1.722 2.042 0.843 0.399 -2.281 5.725
## structuraletiology -0.484 2.429 -0.199 0.842 -5.244 4.277
## priorepilepsy 4.730 1.947 2.429 0.015 0.913 8.546
## status 1.622 2.018 0.804 0.421 -2.332 5.577
## ageyears 0.117 0.189 0.618 0.537 -0.254 0.487
## SEXnumeric 2.085 1.944 1.072 0.284 -1.725 5.896
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.860 0.066 58.475 0.000 47.474 41.712
## arm 0.028 0.039 0.707 0.479 1.028 0.952
## TYPESTATUSnumeric -0.086 0.038 -2.246 0.025 0.918 0.852
## day -0.026 0.037 -0.719 0.472 0.974 0.907
## earlyacademicyear 0.041 0.035 1.178 0.239 1.042 0.973
## white 0.034 0.040 0.835 0.404 1.034 0.956
## structuraletiology -0.009 0.048 -0.182 0.856 0.991 0.903
## priorepilepsy 0.091 0.038 2.411 0.016 1.096 1.017
## status 0.030 0.038 0.801 0.423 1.031 0.957
## ageyears 0.002 0.004 0.629 0.529 1.002 0.995
## SEXnumeric 0.040 0.038 1.056 0.291 1.041 0.966
## upper .95
## intercept 54.031
## arm 1.110
## TYPESTATUSnumeric 0.989
## day 1.046
## earlyacademicyear 1.116
## white 1.119
## structuraletiology 1.088
## priorepilepsy 1.180
## status 1.110
## ageyears 1.009
## SEXnumeric 1.121
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.530 0.351 7.218 0.000 12.553 6.315
## arm -0.209 0.279 -0.749 0.454 0.811 0.469
## TYPESTATUSnumeric 0.529 0.214 2.476 0.013 1.698 1.117
## day 0.169 0.241 0.701 0.483 1.185 0.738
## earlyacademicyear -0.282 0.241 -1.171 0.241 0.754 0.470
## white -0.202 0.232 -0.873 0.383 0.817 0.519
## structuraletiology 0.078 0.273 0.286 0.775 1.081 0.633
## priorepilepsy -0.608 0.272 -2.236 0.025 0.544 0.319
## status -0.231 0.324 -0.713 0.476 0.794 0.421
## ageyears -0.014 0.027 -0.509 0.610 0.986 0.936
## SEXnumeric -0.273 0.237 -1.148 0.251 0.761 0.478
## upper .95
## intercept 24.952
## arm 1.402
## TYPESTATUSnumeric 2.581
## day 1.902
## earlyacademicyear 1.209
## white 1.286
## structuraletiology 1.845
## priorepilepsy 0.928
## status 1.497
## ageyears 1.040
## SEXnumeric 1.213
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 222
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 144 | 78 |
## | 0.649 | 0.351 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 168
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 107 | 61 |
## | 0.637 | 0.363 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 54
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 37 | 17 |
## | 0.685 | 0.315 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore120min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore120min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.6235
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3911767 1.6152067
## sample estimates:
## odds ratio
## 0.8067087
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=120,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.769 -9.795 11.333 0.887
## RMST (arm=1)/(arm=0) 1.008 0.885 1.148 0.908
## RMTL (arm=1)/(arm=0) 0.974 0.739 1.283 0.849
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 82.393 7.813 10.546 0.000 67.079 97.706
## arm 0.769 5.390 0.143 0.887 -9.795 11.333
## TYPESTATUSnumeric -23.407 4.922 -4.755 0.000 -33.054 -13.760
## day -4.854 4.831 -1.005 0.315 -14.323 4.615
## earlyacademicyear 1.179 4.840 0.243 0.808 -8.309 10.666
## white 2.758 5.155 0.535 0.593 -7.346 12.862
## structuraletiology -3.928 6.266 -0.627 0.531 -16.209 8.354
## priorepilepsy 9.607 4.996 1.923 0.055 -0.186 19.399
## status -1.973 5.920 -0.333 0.739 -13.575 9.629
## ageyears 0.472 0.460 1.025 0.305 -0.430 1.373
## SEXnumeric 2.060 4.999 0.412 0.680 -7.738 11.858
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.401 0.098 44.698 0.000 81.531 67.222
## arm 0.008 0.066 0.115 0.908 1.008 0.885
## TYPESTATUSnumeric -0.305 0.068 -4.480 0.000 0.737 0.645
## day -0.058 0.059 -0.981 0.327 0.944 0.840
## earlyacademicyear 0.017 0.060 0.293 0.770 1.018 0.905
## white 0.034 0.066 0.517 0.605 1.035 0.909
## structuraletiology -0.047 0.080 -0.579 0.562 0.955 0.815
## priorepilepsy 0.117 0.062 1.901 0.057 1.125 0.996
## status -0.021 0.074 -0.286 0.775 0.979 0.848
## ageyears 0.006 0.005 1.027 0.304 1.006 0.995
## SEXnumeric 0.026 0.062 0.428 0.668 1.027 0.910
## upper .95
## intercept 98.886
## arm 1.148
## TYPESTATUSnumeric 0.842
## day 1.060
## earlyacademicyear 1.144
## white 1.177
## structuraletiology 1.117
## priorepilepsy 1.269
## status 1.131
## ageyears 1.016
## SEXnumeric 1.159
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.575 0.205 17.477 0.000 35.699 23.908
## arm -0.027 0.141 -0.191 0.849 0.974 0.739
## TYPESTATUSnumeric 0.568 0.123 4.633 0.000 1.764 1.388
## day 0.134 0.130 1.032 0.302 1.143 0.887
## earlyacademicyear -0.015 0.129 -0.119 0.905 0.985 0.765
## white -0.073 0.127 -0.575 0.565 0.930 0.725
## structuraletiology 0.109 0.152 0.720 0.472 1.115 0.829
## priorepilepsy -0.257 0.135 -1.901 0.057 0.774 0.594
## status 0.070 0.155 0.454 0.650 1.073 0.792
## ageyears -0.014 0.014 -0.998 0.318 0.986 0.960
## SEXnumeric -0.048 0.131 -0.362 0.717 0.954 0.737
## upper .95
## intercept 53.306
## arm 1.283
## TYPESTATUSnumeric 2.243
## day 1.475
## earlyacademicyear 1.268
## white 1.192
## structuraletiology 1.501
## priorepilepsy 1.008
## status 1.452
## ageyears 1.013
## SEXnumeric 1.234
# First CI later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 101
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 11 | 90 |
## | 0.109 | 0.891 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 83
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 7 | 76 |
## | 0.084 | 0.916 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 4 | 14 |
## | 0.222 | 0.778 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.1041
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.07110971 1.72835211
## sample estimates:
## odds ratio
## 0.327079
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -3.612 -8.859 1.634 0.177
## RMST (arm=1)/(arm=0) 0.939 0.853 1.033 0.194
## RMTL (arm=1)/(arm=0) 6.055 1.425 25.723 0.015
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 61.577 1.677 36.723 0.000 58.290 64.863
## arm -3.612 2.677 -1.349 0.177 -8.859 1.634
## TYPESTATUSnumeric -1.206 1.649 -0.731 0.465 -4.438 2.026
## day -1.177 1.214 -0.970 0.332 -3.557 1.203
## earlyacademicyear -1.517 1.262 -1.203 0.229 -3.990 0.955
## white 0.458 1.579 0.290 0.772 -2.637 3.553
## structuraletiology -0.927 1.703 -0.544 0.586 -4.265 2.411
## priorepilepsy -1.137 1.381 -0.823 0.410 -3.843 1.569
## status 3.334 1.464 2.278 0.023 0.465 6.203
## ageyears -0.199 0.160 -1.242 0.214 -0.513 0.115
## SEXnumeric -0.072 1.722 -0.042 0.967 -3.447 3.303
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.121 0.029 141.368 0.000 61.649 58.225
## arm -0.063 0.049 -1.300 0.194 0.939 0.853
## TYPESTATUSnumeric -0.021 0.029 -0.708 0.479 0.980 0.926
## day -0.020 0.021 -0.966 0.334 0.980 0.941
## earlyacademicyear -0.026 0.022 -1.196 0.232 0.974 0.934
## white 0.008 0.027 0.295 0.768 1.008 0.955
## structuraletiology -0.016 0.030 -0.537 0.591 0.984 0.928
## priorepilepsy -0.019 0.024 -0.798 0.425 0.981 0.936
## status 0.056 0.025 2.249 0.025 1.058 1.007
## ageyears -0.003 0.003 -1.207 0.227 0.997 0.991
## SEXnumeric -0.002 0.030 -0.055 0.956 0.998 0.942
## upper .95
## intercept 65.275
## arm 1.033
## TYPESTATUSnumeric 1.037
## day 1.021
## earlyacademicyear 1.017
## white 1.064
## structuraletiology 1.044
## priorepilepsy 1.028
## status 1.111
## ageyears 1.002
## SEXnumeric 1.058
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept -2.153 1.087 -1.981 0.048 0.116 0.014
## arm 1.801 0.738 2.440 0.015 6.055 1.425
## TYPESTATUSnumeric 0.888 0.544 1.633 0.102 2.431 0.837
## day 0.737 0.851 0.866 0.386 2.090 0.394
## earlyacademicyear 0.967 0.644 1.502 0.133 2.629 0.745
## white 0.033 0.783 0.043 0.966 1.034 0.223
## structuraletiology 0.343 0.733 0.468 0.640 1.409 0.335
## priorepilepsy 0.890 0.625 1.425 0.154 2.436 0.716
## status -18.307 0.800 -22.889 0.000 0.000 0.000
## ageyears 0.127 0.047 2.685 0.007 1.135 1.035
## SEXnumeric -0.497 0.770 -0.646 0.519 0.608 0.135
## upper .95
## intercept 0.978
## arm 25.723
## TYPESTATUSnumeric 7.060
## day 11.081
## earlyacademicyear 9.283
## white 4.801
## structuraletiology 5.929
## priorepilepsy 8.283
## status 0.000
## ageyears 1.245
## SEXnumeric 2.750
# First CI later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 101
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 73 |
## | 0.277 | 0.723 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 83
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 63 |
## | 0.241 | 0.759 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 8 | 10 |
## | 0.444 | 0.556 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore120min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore120min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.09028
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.122463 1.339615
## sample estimates:
## odds ratio
## 0.4009637
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -13.266 -29.283 2.751 0.105
## RMST (arm=1)/(arm=0) 0.878 0.742 1.039 0.129
## RMTL (arm=1)/(arm=0) 2.334 1.053 5.174 0.037
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 117.482 8.632 13.610 0.000 100.563 134.400
## arm -13.266 8.172 -1.623 0.105 -29.283 2.751
## TYPESTATUSnumeric -7.692 5.938 -1.295 0.195 -19.331 3.947
## day -5.985 5.059 -1.183 0.237 -15.900 3.930
## earlyacademicyear -7.451 5.401 -1.380 0.168 -18.037 3.134
## white 4.986 5.769 0.864 0.387 -6.320 16.293
## structuraletiology -2.905 6.699 -0.434 0.665 -16.035 10.226
## priorepilepsy -5.775 5.421 -1.065 0.287 -16.401 4.850
## status 12.396 5.326 2.328 0.020 1.958 22.833
## ageyears -0.163 0.495 -0.329 0.742 -1.133 0.807
## SEXnumeric -1.588 5.464 -0.291 0.771 -12.297 9.121
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.769 0.080 59.857 0.000 117.794 100.764
## arm -0.130 0.086 -1.517 0.129 0.878 0.742
## TYPESTATUSnumeric -0.072 0.058 -1.232 0.218 0.931 0.830
## day -0.055 0.047 -1.173 0.241 0.946 0.863
## earlyacademicyear -0.070 0.051 -1.373 0.170 0.933 0.844
## white 0.046 0.055 0.846 0.398 1.048 0.941
## structuraletiology -0.028 0.064 -0.442 0.658 0.972 0.857
## priorepilepsy -0.052 0.051 -1.012 0.311 0.949 0.858
## status 0.114 0.050 2.293 0.022 1.121 1.017
## ageyears -0.002 0.005 -0.329 0.742 0.998 0.989
## SEXnumeric -0.016 0.051 -0.320 0.749 0.984 0.890
## upper .95
## intercept 137.702
## arm 1.039
## TYPESTATUSnumeric 1.043
## day 1.038
## earlyacademicyear 1.030
## white 1.166
## structuraletiology 1.102
## priorepilepsy 1.050
## status 1.236
## ageyears 1.008
## SEXnumeric 1.088
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.628 0.785 2.075 0.038 5.093 1.094
## arm 0.848 0.406 2.088 0.037 2.334 1.053
## TYPESTATUSnumeric 0.625 0.362 1.726 0.084 1.868 0.919
## day 0.503 0.437 1.150 0.250 1.654 0.702
## earlyacademicyear 0.610 0.464 1.313 0.189 1.840 0.741
## white -0.430 0.451 -0.954 0.340 0.651 0.269
## structuraletiology 0.156 0.501 0.311 0.756 1.168 0.438
## priorepilepsy 0.551 0.396 1.391 0.164 1.735 0.798
## status -1.096 0.510 -2.149 0.032 0.334 0.123
## ageyears 0.010 0.036 0.280 0.779 1.010 0.941
## SEXnumeric -0.003 0.453 -0.007 0.994 0.997 0.410
## upper .95
## intercept 23.699
## arm 5.174
## TYPESTATUSnumeric 3.799
## day 3.898
## earlyacademicyear 4.573
## white 1.574
## structuraletiology 3.116
## priorepilepsy 3.773
## status 0.908
## ageyears 1.084
## SEXnumeric 2.425
# First CI later than 240 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 101
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 60 | 41 |
## | 0.594 | 0.406 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 83
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 51 | 32 |
## | 0.614 | 0.386 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 9 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore240min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore240min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.4318
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4992948 5.0519405
## sample estimates:
## odds ratio
## 1.586161
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -10.645 -51.237 29.946 0.607
## RMST (arm=1)/(arm=0) 0.934 0.722 1.207 0.601
## RMTL (arm=1)/(arm=0) 1.134 0.678 1.898 0.632
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 202.755 24.543 8.261 0.000 154.651 250.860
## arm -10.645 20.710 -0.514 0.607 -51.237 29.946
## TYPESTATUSnumeric -36.583 16.202 -2.258 0.024 -68.339 -4.828
## day -12.290 14.685 -0.837 0.403 -41.072 16.492
## earlyacademicyear -23.157 14.493 -1.598 0.110 -51.564 5.249
## white 14.733 15.888 0.927 0.354 -16.407 45.873
## structuraletiology -10.943 18.284 -0.599 0.549 -46.778 24.892
## priorepilepsy 3.040 15.755 0.193 0.847 -27.840 33.920
## status 16.192 15.972 1.014 0.311 -15.112 47.496
## ageyears -1.186 1.278 -0.928 0.353 -3.690 1.318
## SEXnumeric -11.396 14.109 -0.808 0.419 -39.049 16.257
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.319 0.138 38.624 0.000 204.092 155.817
## arm -0.069 0.131 -0.523 0.601 0.934 0.722
## TYPESTATUSnumeric -0.227 0.106 -2.147 0.032 0.797 0.647
## day -0.067 0.084 -0.795 0.426 0.935 0.793
## earlyacademicyear -0.135 0.085 -1.589 0.112 0.874 0.740
## white 0.089 0.096 0.921 0.357 1.093 0.905
## structuraletiology -0.069 0.109 -0.634 0.526 0.933 0.754
## priorepilepsy 0.022 0.091 0.240 0.810 1.022 0.855
## status 0.102 0.092 1.108 0.268 1.107 0.925
## ageyears -0.007 0.008 -0.941 0.347 0.993 0.978
## SEXnumeric -0.070 0.082 -0.850 0.395 0.932 0.793
## upper .95
## intercept 267.322
## arm 1.207
## TYPESTATUSnumeric 0.980
## day 1.103
## earlyacademicyear 1.032
## white 1.320
## structuraletiology 1.155
## priorepilepsy 1.222
## status 1.326
## ageyears 1.008
## SEXnumeric 1.096
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.717 0.404 9.204 0.000 41.159 18.650
## arm 0.126 0.263 0.479 0.632 1.134 0.678
## TYPESTATUSnumeric 0.478 0.205 2.328 0.020 1.613 1.078
## day 0.211 0.232 0.911 0.362 1.235 0.784
## earlyacademicyear 0.349 0.223 1.568 0.117 1.418 0.916
## white -0.206 0.223 -0.923 0.356 0.814 0.526
## structuraletiology 0.134 0.264 0.507 0.612 1.143 0.682
## priorepilepsy -0.026 0.239 -0.108 0.914 0.974 0.609
## status -0.196 0.251 -0.781 0.435 0.822 0.503
## ageyears 0.016 0.018 0.864 0.388 1.016 0.980
## SEXnumeric 0.149 0.214 0.694 0.487 1.160 0.762
## upper .95
## intercept 90.836
## arm 1.898
## TYPESTATUSnumeric 2.412
## day 1.946
## earlyacademicyear 2.195
## white 1.260
## structuraletiology 1.916
## priorepilepsy 1.558
## status 1.345
## ageyears 1.054
## SEXnumeric 1.766
## IN THE HOSPITAL
# Patients in each group
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 69 | 37 |
## | 0.651 | 0.349 |
## |-----------|-----------|
##
##
##
##
# Time to first BZD
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 9.00 52.88 24.75 1440.00
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0)
## [1] 165.7452
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 106 106 9 6 15
# Figure time to first BZD
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 4.0 9.0 31.7 26.0 360.0
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 5.00 9.00 92.38 23.00 1440.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=0 69 37.1 35.2
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=1 37 18.4 20.3
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=0 0.103 0.439
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=1 0.179 0.439
##
## Chisq= 0.4 on 1 degrees of freedom, p= 0.5
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.5073782
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 106, number of events= 106
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 -0.123640
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.304963
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.156174
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.242866
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.128864
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.001492
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy -0.033578
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.245805
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.011140
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.076751
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.883698
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.737151
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.169030
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.274898
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.879093
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.998509
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.966979
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.278650
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.988922
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.926120
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.234115
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.253876
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.219812
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.210579
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.219801
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.232757
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.257892
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.304712
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.019597
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.234414
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 -0.528
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -1.201
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.710
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.153
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.586
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.006
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy -0.130
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.807
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.568
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.327
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.597
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.230
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.477
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.249
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.558
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.995
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.896
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.420
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.570
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.743
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.8837
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.7372
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.1690
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.2749
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.8791
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.9985
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.9670
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.2786
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9889
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.9261
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.1316
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.3566
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.8554
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.7844
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.1375
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.0015
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.0341
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.7821
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0112
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.0798
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.5585
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.4482
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.7598
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.8438
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.5714
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.6327
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.5833
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.7037
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9517
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.5850
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.398
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.212
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.799
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.926
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.352
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.576
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.603
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.323
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.028
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.466
##
## Concordance= 0.591 (se = 0.035 )
## Rsquare= 0.071 (max possible= 0.999 )
## Likelihood ratio test= 7.86 on 10 df, p=0.6
## Wald test = 7.93 on 10 df, p=0.6
## Score (logrank) test = 8.05 on 10 df, p=0.6
# Time to first non-BZD AED
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 22.25 40.50 99.53 85.25 1488.00
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0)
## [1] 212.08
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 106.0 106.0 40.5 29.0 51.0
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 20.00 42.00 72.51 76.00 503.00
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 24.0 31.0 149.9 96.0 1488.0
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=0 69 35.8 34.6
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=1 37 18.3 19.5
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=0 0.0402 0.169
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=1 0.0714 0.169
##
## Chisq= 0.2 on 1 degrees of freedom, p= 0.7
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.6807389
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 106, number of events= 106
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.07638
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.16562
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.48198
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.23556
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.24809
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.72974
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.01601
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.40781
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.02949
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.25784
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.07937
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.84737
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.61928
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.26562
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.78029
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 2.07454
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.01613
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.50351
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.97094
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.77272
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.23979
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.24964
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.24084
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.21482
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.22920
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.23838
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.26260
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.30050
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.01972
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.22674
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.319
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.663
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.001
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.097
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -1.082
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 3.061
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.061
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.357
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -1.495
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -1.137
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.7501
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.5070
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.0454 *
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.2728
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.2791
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.0022 **
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.9514
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.1748
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.1348
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.2555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.0794
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.8474
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.6193
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.2656
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.7803
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 2.0745
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.0161
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.5035
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9709
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.7727
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.9265
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.1801
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.6176
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.7901
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.2816
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.4820
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.9841
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.6651
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0299
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.2941
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.6746
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.5195
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.0100
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.8307
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.4979
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.3002
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.6073
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.8343
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9341
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.4955
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.727
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.382
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.596
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.928
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.223
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 3.310
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.700
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.710
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.009
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.205
##
## Concordance= 0.618 (se = 0.034 )
## Rsquare= 0.17 (max possible= 0.999 )
## Likelihood ratio test= 19.79 on 10 df, p=0.03
## Wald test = 19.82 on 10 df, p=0.03
## Score (logrank) test = 20.08 on 10 df, p=0.03
# Time to first CI
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 113.0 175.0 558.1 420.0 7200.0 55
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## 1)
##
## 55 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 51 51 175 122 253
# Figure time to first CI
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 120.0 186.0 562.3 487.5 7200.0 37
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 90.5 135.0 551.1 326.5 6003.0 18
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017, rho = 1)
##
## n=51, 55 observations deleted due to missingness.
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=0 32 15.6 17.3
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=1 19 10.6 8.9
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=0 0.172 0.756
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017=1 0.334 0.756
##
## Chisq= 0.8 on 1 degrees of freedom, p= 0.4
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.3845
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 51, number of events= 51
## (55 observations deleted due to missingness)
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.313739
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.574764
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.136634
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.103404
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.011430
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.089874
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy -0.001939
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.868622
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.024956
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.080351
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.368532
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.562838
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.146409
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.108940
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.988635
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.914046
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.998063
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.383625
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.975353
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.083667
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.363683
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.361937
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.353350
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.311759
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.345741
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.335300
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.454676
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.554866
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.031960
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.346770
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.863
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -1.588
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.387
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.332
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.033
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.268
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy -0.004
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.565
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.781
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.232
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.388
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.112
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.699
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.740
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.974
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.789
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.997
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.117
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.435
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.817
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.3685
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.5628
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.1464
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.1089
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.9886
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.9140
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.9981
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.3836
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9754
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.0837
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.7307
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.7767
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.8723
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.9018
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.0115
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.0940
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.0019
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.4195
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0253
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.9228
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.6709
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.2769
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.5735
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.6019
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.5020
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.4738
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.4094
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.8034
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9161
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.5492
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 2.791
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.144
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.291
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 2.043
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.947
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.764
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 2.433
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 7.072
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.038
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 2.138
##
## Concordance= 0.597 (se = 0.049 )
## Rsquare= 0.148 (max possible= 0.997 )
## Likelihood ratio test= 8.14 on 10 df, p=0.6
## Wald test = 8.1 on 10 df, p=0.6
## Score (logrank) test = 8.45 on 10 df, p=0.6
#### Recommendations and outliers in the hospital
# First BZD later than 20 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 72 | 34 |
## | 0.679 | 0.321 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 69
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 47 | 22 |
## | 0.681 | 0.319 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 37
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 25 | 12 |
## | 0.676 | 0.324 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore20min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore20min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3934397 2.5974709
## sample estimates:
## odds ratio
## 1.025226
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=20,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.423 -2.307 3.153 0.762
## RMST (arm=1)/(arm=0) 1.048 0.820 1.339 0.710
## RMTL (arm=1)/(arm=0) 0.964 0.706 1.317 0.819
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 9.595 2.096 4.579 0.000 5.487 13.702
## arm 0.423 1.393 0.303 0.762 -2.307 3.153
## TYPESTATUSnumeric -0.905 1.552 -0.583 0.560 -3.948 2.137
## day -1.626 1.429 -1.137 0.255 -4.427 1.176
## earlyacademicyear -1.541 1.398 -1.103 0.270 -4.281 1.199
## white 0.860 1.509 0.570 0.569 -2.097 3.817
## structuraletiology -0.437 1.560 -0.280 0.779 -3.495 2.621
## priorepilepsy 2.258 1.680 1.344 0.179 -1.034 5.551
## status -1.578 1.791 -0.881 0.378 -5.089 1.933
## ageyears 0.235 0.140 1.673 0.094 -0.040 0.510
## SEXnumeric 1.180 1.434 0.823 0.411 -1.630 3.990
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.255 0.199 11.358 0.000 9.532 6.460
## arm 0.047 0.125 0.372 0.710 1.048 0.820
## TYPESTATUSnumeric -0.087 0.150 -0.580 0.562 0.917 0.683
## day -0.136 0.129 -1.050 0.294 0.873 0.677
## earlyacademicyear -0.137 0.132 -1.040 0.299 0.872 0.674
## white 0.070 0.146 0.483 0.629 1.073 0.806
## structuraletiology -0.034 0.146 -0.232 0.817 0.967 0.726
## priorepilepsy 0.189 0.147 1.282 0.200 1.208 0.905
## status -0.117 0.155 -0.757 0.449 0.889 0.657
## ageyears 0.020 0.012 1.665 0.096 1.020 0.996
## SEXnumeric 0.100 0.132 0.761 0.447 1.105 0.854
## upper .95
## intercept 14.065
## arm 1.339
## TYPESTATUSnumeric 1.230
## day 1.125
## earlyacademicyear 1.129
## white 1.427
## structuraletiology 1.287
## priorepilepsy 1.612
## status 1.205
## ageyears 1.045
## SEXnumeric 1.431
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.330 0.231 10.096 0.000 10.283 6.541
## arm -0.036 0.159 -0.228 0.819 0.964 0.706
## TYPESTATUSnumeric 0.096 0.168 0.572 0.567 1.101 0.792
## day 0.200 0.165 1.211 0.226 1.221 0.884
## earlyacademicyear 0.181 0.156 1.163 0.245 1.198 0.883
## white -0.110 0.162 -0.679 0.497 0.896 0.651
## structuraletiology 0.057 0.171 0.333 0.739 1.059 0.757
## priorepilepsy -0.285 0.215 -1.328 0.184 0.752 0.493
## status 0.224 0.231 0.972 0.331 1.251 0.796
## ageyears -0.028 0.018 -1.603 0.109 0.972 0.939
## SEXnumeric -0.144 0.160 -0.898 0.369 0.866 0.633
## upper .95
## intercept 16.166
## arm 1.317
## TYPESTATUSnumeric 1.530
## day 1.686
## earlyacademicyear 1.626
## white 1.231
## structuraletiology 1.479
## priorepilepsy 1.146
## status 1.966
## ageyears 1.006
## SEXnumeric 1.185
# First BZD later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 87 | 19 |
## | 0.821 | 0.179 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 69
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 55 | 14 |
## | 0.797 | 0.203 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 37
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 32 | 5 |
## | 0.865 | 0.135 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore40min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore40min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.4385
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1586844 2.0307057
## sample estimates:
## odds ratio
## 0.6165311
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.502 -5.650 4.646 0.848
## RMST (arm=1)/(arm=0) 0.988 0.708 1.379 0.945
## RMTL (arm=1)/(arm=0) 1.029 0.832 1.273 0.790
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 14.267 4.395 3.246 0.001 5.652 22.881
## arm -0.502 2.627 -0.191 0.848 -5.650 4.646
## TYPESTATUSnumeric -4.775 2.611 -1.829 0.067 -9.893 0.343
## day -2.112 2.850 -0.741 0.459 -7.698 3.474
## earlyacademicyear -2.608 2.741 -0.951 0.341 -7.981 2.764
## white 1.951 2.963 0.659 0.510 -3.855 7.758
## structuraletiology 0.249 3.056 0.081 0.935 -5.741 6.239
## priorepilepsy 2.770 3.493 0.793 0.428 -4.076 9.617
## status -4.191 3.465 -1.209 0.227 -10.982 2.601
## ageyears 0.495 0.273 1.810 0.070 -0.041 1.031
## SEXnumeric 0.582 2.684 0.217 0.828 -4.678 5.843
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.628 0.295 8.906 0.000 13.841 7.763
## arm -0.012 0.170 -0.069 0.945 0.988 0.708
## TYPESTATUSnumeric -0.342 0.197 -1.737 0.082 0.710 0.483
## day -0.109 0.180 -0.605 0.545 0.897 0.630
## earlyacademicyear -0.163 0.182 -0.896 0.370 0.850 0.595
## white 0.110 0.201 0.549 0.583 1.117 0.753
## structuraletiology 0.028 0.197 0.142 0.887 1.028 0.699
## priorepilepsy 0.156 0.208 0.751 0.453 1.169 0.777
## status -0.224 0.217 -1.035 0.301 0.799 0.523
## ageyears 0.030 0.016 1.849 0.065 1.030 0.998
## SEXnumeric 0.026 0.172 0.150 0.881 1.026 0.732
## upper .95
## intercept 24.679
## arm 1.379
## TYPESTATUSnumeric 1.045
## day 1.276
## earlyacademicyear 1.213
## white 1.656
## structuraletiology 1.512
## priorepilepsy 1.759
## status 1.222
## ageyears 1.064
## SEXnumeric 1.438
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.236 0.180 17.943 0.000 25.428 17.857
## arm 0.029 0.108 0.267 0.790 1.029 0.832
## TYPESTATUSnumeric 0.188 0.103 1.821 0.069 1.206 0.986
## day 0.098 0.120 0.814 0.416 1.103 0.872
## earlyacademicyear 0.111 0.113 0.977 0.328 1.117 0.895
## white -0.088 0.121 -0.729 0.466 0.916 0.723
## structuraletiology -0.006 0.127 -0.048 0.962 0.994 0.776
## priorepilepsy -0.125 0.157 -0.798 0.425 0.883 0.649
## status 0.193 0.154 1.254 0.210 1.213 0.897
## ageyears -0.021 0.012 -1.718 0.086 0.979 0.955
## SEXnumeric -0.030 0.111 -0.268 0.789 0.971 0.781
## upper .95
## intercept 36.210
## arm 1.273
## TYPESTATUSnumeric 1.476
## day 1.395
## earlyacademicyear 1.395
## white 1.160
## structuraletiology 1.274
## priorepilepsy 1.200
## status 1.641
## ageyears 1.003
## SEXnumeric 1.206
# First BZD later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 91 | 15 |
## | 0.858 | 0.142 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 69
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 59 | 10 |
## | 0.855 | 0.145 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 37
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 32 | 5 |
## | 0.865 | 0.135 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2271364 3.2802464
## sample estimates:
## odds ratio
## 0.9225637
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.939 -8.180 6.302 0.799
## RMST (arm=1)/(arm=0) 0.974 0.654 1.449 0.895
## RMTL (arm=1)/(arm=0) 1.028 0.863 1.225 0.757
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 20.364 6.565 3.102 0.002 7.497 33.230
## arm -0.939 3.694 -0.254 0.799 -8.180 6.302
## TYPESTATUSnumeric -7.297 3.469 -2.103 0.035 -14.095 -0.498
## day -2.587 4.110 -0.630 0.529 -10.643 5.468
## earlyacademicyear -5.057 3.947 -1.281 0.200 -12.792 2.679
## white 1.567 4.359 0.360 0.719 -6.976 10.110
## structuraletiology 0.566 4.293 0.132 0.895 -7.848 8.980
## priorepilepsy 1.059 5.089 0.208 0.835 -8.915 11.034
## status -5.692 4.615 -1.233 0.217 -14.737 3.353
## ageyears 0.663 0.398 1.663 0.096 -0.118 1.443
## SEXnumeric -0.724 3.872 -0.187 0.852 -8.314 6.866
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.972 0.355 8.363 0.000 19.528 9.732
## arm -0.027 0.203 -0.132 0.895 0.974 0.654
## TYPESTATUSnumeric -0.454 0.235 -1.933 0.053 0.635 0.401
## day -0.109 0.214 -0.507 0.612 0.897 0.589
## earlyacademicyear -0.268 0.218 -1.228 0.219 0.765 0.499
## white 0.064 0.243 0.265 0.791 1.067 0.662
## structuraletiology 0.046 0.232 0.200 0.841 1.048 0.665
## priorepilepsy 0.043 0.251 0.169 0.866 1.043 0.638
## status -0.271 0.255 -1.061 0.289 0.763 0.463
## ageyears 0.033 0.019 1.717 0.086 1.034 0.995
## SEXnumeric -0.047 0.206 -0.226 0.821 0.954 0.638
## upper .95
## intercept 39.187
## arm 1.449
## TYPESTATUSnumeric 1.006
## day 1.365
## earlyacademicyear 1.173
## white 1.718
## structuraletiology 1.651
## priorepilepsy 1.708
## status 1.258
## ageyears 1.074
## SEXnumeric 1.429
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.671 0.162 22.610 0.000 39.276 28.572
## arm 0.028 0.089 0.309 0.757 1.028 0.863
## TYPESTATUSnumeric 0.170 0.081 2.105 0.035 1.185 1.012
## day 0.069 0.102 0.679 0.497 1.072 0.877
## earlyacademicyear 0.125 0.097 1.281 0.200 1.133 0.936
## white -0.043 0.106 -0.405 0.685 0.958 0.779
## structuraletiology -0.011 0.105 -0.106 0.916 0.989 0.806
## priorepilepsy -0.029 0.130 -0.223 0.824 0.971 0.753
## status 0.146 0.116 1.258 0.208 1.157 0.922
## ageyears -0.017 0.010 -1.593 0.111 0.984 0.964
## SEXnumeric 0.015 0.095 0.161 0.872 1.015 0.844
## upper .95
## intercept 53.991
## arm 1.225
## TYPESTATUSnumeric 1.388
## day 1.309
## earlyacademicyear 1.371
## white 1.179
## structuraletiology 1.214
## priorepilepsy 1.254
## status 1.453
## ageyears 1.004
## SEXnumeric 1.222
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 53 | 53 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 69
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 33 | 36 |
## | 0.478 | 0.522 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 37
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 17 |
## | 0.541 | 0.459 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore40min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore40min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.6839
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3232202 1.8703559
## sample estimates:
## odds ratio
## 0.781013
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.093 -4.175 3.989 0.964
## RMST (arm=1)/(arm=0) 0.995 0.869 1.140 0.943
## RMTL (arm=1)/(arm=0) 0.985 0.648 1.498 0.945
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 28.139 3.408 8.257 0.000 21.459 34.818
## arm -0.093 2.083 -0.045 0.964 -4.175 3.989
## TYPESTATUSnumeric 2.906 2.489 1.168 0.243 -1.972 7.784
## day -4.501 2.169 -2.076 0.038 -8.751 -0.251
## earlyacademicyear 0.149 2.135 0.070 0.944 -4.035 4.332
## white 3.417 2.224 1.536 0.125 -0.943 7.776
## structuraletiology -3.291 2.275 -1.447 0.148 -7.750 1.168
## priorepilepsy 1.592 2.501 0.637 0.524 -3.309 6.493
## status -2.933 2.832 -1.036 0.300 -8.483 2.617
## ageyears 0.446 0.211 2.110 0.035 0.032 0.860
## SEXnumeric 0.332 2.303 0.144 0.885 -4.183 4.846
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.333 0.115 28.884 0.000 28.029 22.355
## arm -0.005 0.069 -0.072 0.943 0.995 0.869
## TYPESTATUSnumeric 0.096 0.080 1.196 0.232 1.101 0.940
## day -0.149 0.073 -2.048 0.041 0.862 0.748
## earlyacademicyear 0.010 0.071 0.133 0.894 1.010 0.878
## white 0.117 0.077 1.527 0.127 1.124 0.967
## structuraletiology -0.113 0.079 -1.421 0.155 0.893 0.765
## priorepilepsy 0.049 0.079 0.617 0.537 1.050 0.899
## status -0.095 0.093 -1.021 0.307 0.910 0.758
## ageyears 0.014 0.007 2.091 0.036 1.014 1.001
## SEXnumeric 0.010 0.077 0.124 0.901 1.010 0.869
## upper .95
## intercept 35.143
## arm 1.140
## TYPESTATUSnumeric 1.289
## day 0.994
## earlyacademicyear 1.161
## white 1.307
## structuraletiology 1.044
## priorepilepsy 1.227
## status 1.091
## ageyears 1.028
## SEXnumeric 1.173
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.436 0.353 6.911 0.000 11.432 5.728
## arm -0.015 0.214 -0.068 0.945 0.985 0.648
## TYPESTATUSnumeric -0.304 0.297 -1.026 0.305 0.738 0.412
## day 0.477 0.239 1.995 0.046 1.612 1.008
## earlyacademicyear 0.033 0.220 0.152 0.879 1.034 0.672
## white -0.321 0.225 -1.422 0.155 0.726 0.467
## structuraletiology 0.307 0.216 1.422 0.155 1.359 0.890
## priorepilepsy -0.198 0.328 -0.605 0.545 0.820 0.431
## status 0.337 0.335 1.005 0.315 1.400 0.726
## ageyears -0.055 0.029 -1.936 0.053 0.946 0.895
## SEXnumeric -0.052 0.235 -0.220 0.826 0.950 0.599
## upper .95
## intercept 22.813
## arm 1.498
## TYPESTATUSnumeric 1.320
## day 2.576
## earlyacademicyear 1.592
## white 1.129
## structuraletiology 2.074
## priorepilepsy 1.559
## status 2.698
## ageyears 1.001
## SEXnumeric 1.506
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 70 | 36 |
## | 0.660 | 0.340 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 69
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 45 | 24 |
## | 0.652 | 0.348 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 37
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 25 | 12 |
## | 0.676 | 0.324 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.8334
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3478949 2.2616493
## sample estimates:
## odds ratio
## 0.9008993
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.401 -9.368 4.566 0.499
## RMST (arm=1)/(arm=0) 0.934 0.778 1.120 0.462
## RMTL (arm=1)/(arm=0) 1.094 0.787 1.520 0.592
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 37.865 5.776 6.556 0.000 26.545 49.185
## arm -2.401 3.555 -0.675 0.499 -9.368 4.566
## TYPESTATUSnumeric 1.431 4.331 0.330 0.741 -7.058 9.920
## day -8.797 3.671 -2.396 0.017 -15.992 -1.602
## earlyacademicyear 1.283 3.637 0.353 0.724 -5.846 8.412
## white 5.688 3.792 1.500 0.134 -1.744 13.120
## structuraletiology -7.786 3.679 -2.116 0.034 -14.997 -0.574
## priorepilepsy 1.237 4.340 0.285 0.776 -7.269 9.744
## status -5.466 4.810 -1.136 0.256 -14.894 3.961
## ageyears 0.695 0.355 1.961 0.050 0.000 1.391
## SEXnumeric 2.346 3.818 0.614 0.539 -5.138 9.830
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.624 0.152 23.802 0.000 37.479 27.810
## arm -0.068 0.093 -0.736 0.462 0.934 0.778
## TYPESTATUSnumeric 0.040 0.111 0.360 0.719 1.041 0.837
## day -0.230 0.096 -2.406 0.016 0.795 0.659
## earlyacademicyear 0.044 0.096 0.453 0.651 1.045 0.865
## white 0.156 0.103 1.512 0.131 1.169 0.955
## structuraletiology -0.217 0.103 -2.099 0.036 0.805 0.658
## priorepilepsy 0.026 0.108 0.240 0.810 1.026 0.831
## status -0.145 0.127 -1.139 0.255 0.865 0.675
## ageyears 0.017 0.009 1.956 0.051 1.017 1.000
## SEXnumeric 0.061 0.098 0.616 0.538 1.062 0.876
## upper .95
## intercept 50.511
## arm 1.120
## TYPESTATUSnumeric 1.294
## day 0.958
## earlyacademicyear 1.261
## white 1.431
## structuraletiology 0.986
## priorepilepsy 1.268
## status 1.110
## ageyears 1.034
## SEXnumeric 1.289
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.063 0.275 11.124 0.000 21.388 12.468
## arm 0.090 0.168 0.535 0.592 1.094 0.787
## TYPESTATUSnumeric -0.055 0.212 -0.259 0.796 0.947 0.625
## day 0.416 0.189 2.205 0.027 1.516 1.047
## earlyacademicyear -0.028 0.171 -0.164 0.870 0.972 0.695
## white -0.244 0.176 -1.392 0.164 0.783 0.555
## structuraletiology 0.328 0.164 1.995 0.046 1.388 1.006
## priorepilepsy -0.069 0.234 -0.297 0.767 0.933 0.590
## status 0.253 0.236 1.071 0.284 1.288 0.811
## ageyears -0.038 0.020 -1.849 0.065 0.963 0.925
## SEXnumeric -0.111 0.183 -0.608 0.544 0.895 0.625
## upper .95
## intercept 36.688
## arm 1.520
## TYPESTATUSnumeric 1.434
## day 2.193
## earlyacademicyear 1.360
## white 1.105
## structuraletiology 1.914
## priorepilepsy 1.476
## status 2.045
## ageyears 1.002
## SEXnumeric 1.281
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 89 | 17 |
## | 0.840 | 0.160 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 69
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 59 | 10 |
## | 0.855 | 0.145 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 37
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 30 | 7 |
## | 0.811 | 0.189 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore120min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore120min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.586
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4005423 4.4694842
## sample estimates:
## odds ratio
## 1.372337
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=120,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.918 -16.262 12.426 0.793
## RMST (arm=1)/(arm=0) 0.958 0.735 1.249 0.751
## RMTL (arm=1)/(arm=0) 1.024 0.821 1.277 0.835
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 56.481 12.457 4.534 0.000 32.065 80.897
## arm -1.918 7.319 -0.262 0.793 -16.262 12.426
## TYPESTATUSnumeric -10.061 7.903 -1.273 0.203 -25.550 5.428
## day -16.775 7.358 -2.280 0.023 -31.197 -2.352
## earlyacademicyear -1.449 7.715 -0.188 0.851 -16.571 13.673
## white 8.395 7.577 1.108 0.268 -6.455 23.245
## structuraletiology -16.673 7.061 -2.361 0.018 -30.512 -2.833
## priorepilepsy 1.402 9.305 0.151 0.880 -16.834 19.639
## status -9.522 10.218 -0.932 0.351 -29.549 10.504
## ageyears 1.428 0.722 1.979 0.048 0.013 2.843
## SEXnumeric 6.520 7.583 0.860 0.390 -8.342 21.382
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.018 0.235 17.103 0.000 55.600 35.083
## arm -0.043 0.135 -0.317 0.751 0.958 0.735
## TYPESTATUSnumeric -0.194 0.162 -1.203 0.229 0.823 0.600
## day -0.310 0.136 -2.278 0.023 0.734 0.562
## earlyacademicyear -0.009 0.151 -0.057 0.955 0.991 0.737
## white 0.169 0.150 1.128 0.259 1.185 0.882
## structuraletiology -0.347 0.147 -2.357 0.018 0.707 0.530
## priorepilepsy 0.005 0.168 0.029 0.977 1.005 0.724
## status -0.176 0.199 -0.883 0.377 0.839 0.568
## ageyears 0.024 0.012 1.974 0.048 1.024 1.000
## SEXnumeric 0.120 0.138 0.873 0.383 1.128 0.861
## upper .95
## intercept 88.116
## arm 1.249
## TYPESTATUSnumeric 1.130
## day 0.958
## earlyacademicyear 1.333
## white 1.590
## structuraletiology 0.943
## priorepilepsy 1.396
## status 1.239
## ageyears 1.049
## SEXnumeric 1.477
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.139 0.191 21.697 0.000 62.728 43.161
## arm 0.024 0.113 0.209 0.835 1.024 0.821
## TYPESTATUSnumeric 0.151 0.115 1.310 0.190 1.162 0.928
## day 0.260 0.120 2.170 0.030 1.296 1.025
## earlyacademicyear 0.033 0.116 0.286 0.775 1.034 0.824
## white -0.121 0.113 -1.071 0.284 0.886 0.709
## structuraletiology 0.235 0.104 2.252 0.024 1.265 1.031
## priorepilepsy -0.031 0.150 -0.208 0.835 0.969 0.722
## status 0.147 0.158 0.931 0.352 1.158 0.850
## ageyears -0.024 0.013 -1.894 0.058 0.977 0.953
## SEXnumeric -0.101 0.118 -0.852 0.394 0.904 0.718
## upper .95
## intercept 91.166
## arm 1.277
## TYPESTATUSnumeric 1.456
## day 1.639
## earlyacademicyear 1.297
## white 1.106
## structuraletiology 1.553
## priorepilepsy 1.301
## status 1.579
## ageyears 1.001
## SEXnumeric 1.140
# First CI later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 51
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 45 |
## | 0.118 | 0.882 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 32
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 4 | 28 |
## | 0.125 | 0.875 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 19
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 2 | 17 |
## | 0.105 | 0.895 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1537173 14.7214553
## sample estimates:
## odds ratio
## 1.209752
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.759 -9.410 7.893 0.864
## RMST (arm=1)/(arm=0) 0.986 0.843 1.153 0.856
## RMTL (arm=1)/(arm=0) 0.264 0.041 1.694 0.160
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 54.624 4.340 12.585 0.000 46.117 63.131
## arm -0.759 4.414 -0.172 0.864 -9.410 7.893
## TYPESTATUSnumeric 3.860 3.431 1.125 0.261 -2.864 10.585
## day -6.700 3.667 -1.827 0.068 -13.888 0.488
## earlyacademicyear 1.881 3.206 0.587 0.557 -4.403 8.165
## white 1.302 4.130 0.315 0.753 -6.793 9.396
## structuraletiology -2.124 4.832 -0.440 0.660 -11.594 7.346
## priorepilepsy 4.855 3.240 1.499 0.134 -1.495 11.205
## status 0.565 2.090 0.270 0.787 -3.532 4.662
## ageyears 0.255 0.279 0.914 0.361 -0.292 0.803
## SEXnumeric 1.687 4.715 0.358 0.720 -7.553 10.927
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.997 0.081 49.232 0.000 54.442 46.433
## arm -0.014 0.080 -0.181 0.856 0.986 0.843
## TYPESTATUSnumeric 0.069 0.063 1.099 0.272 1.072 0.947
## day -0.119 0.070 -1.692 0.091 0.888 0.774
## earlyacademicyear 0.035 0.059 0.587 0.557 1.035 0.922
## white 0.026 0.079 0.327 0.744 1.026 0.879
## structuraletiology -0.039 0.090 -0.428 0.669 0.962 0.806
## priorepilepsy 0.083 0.058 1.425 0.154 1.087 0.969
## status 0.011 0.038 0.303 0.762 1.012 0.939
## ageyears 0.004 0.005 0.878 0.380 1.004 0.994
## SEXnumeric 0.029 0.085 0.343 0.732 1.030 0.871
## upper .95
## intercept 63.832
## arm 1.153
## TYPESTATUSnumeric 1.213
## day 1.019
## earlyacademicyear 1.162
## white 1.197
## structuraletiology 1.148
## priorepilepsy 1.219
## status 1.089
## ageyears 1.015
## SEXnumeric 1.217
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef)
## intercept -21.893 3.808 -5.749 0.000 0.000000e+00
## arm -1.330 0.948 -1.404 0.160 2.640000e-01
## TYPESTATUSnumeric -3.307 1.997 -1.656 0.098 3.700000e-02
## day 23.591 2.769 8.521 0.000 1.760421e+10
## earlyacademicyear 1.008 1.121 0.899 0.369 2.740000e+00
## white 2.979 1.805 1.651 0.099 1.967000e+01
## structuraletiology 3.216 1.854 1.735 0.083 2.493600e+01
## priorepilepsy -20.872 1.390 -15.011 0.000 0.000000e+00
## status -0.251 1.299 -0.194 0.846 7.780000e-01
## ageyears -0.187 0.118 -1.582 0.114 8.300000e-01
## SEXnumeric -1.582 0.977 -1.618 0.106 2.060000e-01
## lower .95 upper .95
## intercept 0.000 0.000000e+00
## arm 0.041 1.694000e+00
## TYPESTATUSnumeric 0.001 1.834000e+00
## day 77441672.292 4.001829e+12
## earlyacademicyear 0.304 2.466700e+01
## white 0.572 6.760250e+02
## structuraletiology 0.659 9.433340e+02
## priorepilepsy 0.000 0.000000e+00
## status 0.061 9.915000e+00
## ageyears 0.658 1.046000e+00
## SEXnumeric 0.030 1.396000e+00
# First CI later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 51
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 17 | 34 |
## | 0.333 | 0.667 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 32
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 23 |
## | 0.281 | 0.719 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 19
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 8 | 11 |
## | 0.421 | 0.579 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore120min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore120min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.3652
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1395128 2.1093868
## sample estimates:
## odds ratio
## 0.5448555
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -7.833 -27.168 11.502 0.427
## RMST (arm=1)/(arm=0) 0.926 0.764 1.122 0.431
## RMTL (arm=1)/(arm=0) 1.486 0.473 4.669 0.498
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 103.224 10.846 9.517 0.000 81.966 124.482
## arm -7.833 9.865 -0.794 0.427 -27.168 11.502
## TYPESTATUSnumeric 10.915 8.485 1.286 0.198 -5.715 27.545
## day -12.174 8.969 -1.357 0.175 -29.753 5.405
## earlyacademicyear 4.246 8.321 0.510 0.610 -12.063 20.555
## white -2.149 9.744 -0.221 0.825 -21.247 16.948
## structuraletiology -6.228 11.043 -0.564 0.573 -27.872 15.417
## priorepilepsy 6.163 9.201 0.670 0.503 -11.872 24.197
## status 1.701 8.487 0.200 0.841 -14.934 18.336
## ageyears 0.957 0.678 1.412 0.158 -0.371 2.285
## SEXnumeric 4.400 10.040 0.438 0.661 -15.278 24.077
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.632 0.107 43.148 0.000 102.719 83.229
## arm -0.077 0.098 -0.788 0.431 0.926 0.764
## TYPESTATUSnumeric 0.107 0.083 1.276 0.202 1.112 0.945
## day -0.117 0.091 -1.284 0.199 0.889 0.744
## earlyacademicyear 0.043 0.080 0.531 0.596 1.044 0.891
## white -0.018 0.098 -0.181 0.856 0.982 0.810
## structuraletiology -0.061 0.111 -0.549 0.583 0.941 0.757
## priorepilepsy 0.056 0.089 0.634 0.526 1.058 0.889
## status 0.018 0.080 0.226 0.821 1.018 0.870
## ageyears 0.009 0.007 1.370 0.171 1.009 0.996
## SEXnumeric 0.041 0.098 0.420 0.674 1.042 0.860
## upper .95
## intercept 126.773
## arm 1.122
## TYPESTATUSnumeric 1.310
## day 1.064
## earlyacademicyear 1.222
## white 1.191
## structuraletiology 1.169
## priorepilepsy 1.259
## status 1.191
## ageyears 1.022
## SEXnumeric 1.263
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.540 0.762 3.334 0.001 12.679 2.849
## arm 0.396 0.584 0.678 0.498 1.486 0.473
## TYPESTATUSnumeric -0.708 0.702 -1.009 0.313 0.493 0.125
## day 0.847 0.563 1.505 0.132 2.333 0.774
## earlyacademicyear -0.205 0.667 -0.307 0.759 0.815 0.220
## white 0.357 0.537 0.664 0.507 1.429 0.498
## structuraletiology 0.378 0.659 0.574 0.566 1.459 0.401
## priorepilepsy -0.492 0.669 -0.736 0.462 0.611 0.165
## status -0.114 0.790 -0.144 0.885 0.892 0.190
## ageyears -0.071 0.054 -1.304 0.192 0.932 0.838
## SEXnumeric -0.316 0.580 -0.545 0.586 0.729 0.234
## upper .95
## intercept 56.429
## arm 4.669
## TYPESTATUSnumeric 1.949
## day 7.026
## earlyacademicyear 3.013
## white 4.097
## structuraletiology 5.311
## priorepilepsy 2.267
## status 4.200
## ageyears 1.036
## SEXnumeric 2.272
# First CI later than 240 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 51
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 33 | 18 |
## | 0.647 | 0.353 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 32
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 19 | 13 |
## | 0.594 | 0.406 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 19
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 14 | 5 |
## | 0.737 | 0.263 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore240min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore240min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.3727
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1187173 2.0687742
## sample estimates:
## odds ratio
## 0.5285584
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -24.810 -69.836 20.216 0.280
## RMST (arm=1)/(arm=0) 0.853 0.639 1.138 0.280
## RMTL (arm=1)/(arm=0) 1.357 0.747 2.463 0.316
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 182.039 30.490 5.970 0.000 122.280 241.799
## arm -24.810 22.973 -1.080 0.280 -69.836 20.216
## TYPESTATUSnumeric -3.740 21.876 -0.171 0.864 -46.615 39.136
## day -24.992 23.305 -1.072 0.284 -70.669 20.686
## earlyacademicyear 7.286 21.852 0.333 0.739 -35.542 50.114
## white -16.266 23.180 -0.702 0.483 -61.698 29.167
## structuraletiology 3.716 25.839 0.144 0.886 -46.927 54.359
## priorepilepsy -11.197 30.601 -0.366 0.714 -71.174 48.779
## status -9.737 29.021 -0.336 0.737 -66.617 47.143
## ageyears 2.953 1.700 1.738 0.082 -0.378 6.285
## SEXnumeric 0.608 22.852 0.027 0.979 -44.182 45.397
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.203 0.186 28.029 0.000 181.853 126.389
## arm -0.159 0.147 -1.080 0.280 0.853 0.639
## TYPESTATUSnumeric -0.016 0.143 -0.113 0.910 0.984 0.744
## day -0.155 0.146 -1.065 0.287 0.856 0.644
## earlyacademicyear 0.048 0.134 0.360 0.719 1.049 0.807
## white -0.098 0.139 -0.702 0.482 0.907 0.690
## structuraletiology 0.023 0.160 0.146 0.884 1.024 0.748
## priorepilepsy -0.076 0.190 -0.401 0.689 0.927 0.638
## status -0.052 0.186 -0.278 0.781 0.950 0.660
## ageyears 0.017 0.010 1.715 0.086 1.018 0.998
## SEXnumeric 0.006 0.138 0.047 0.962 1.007 0.768
## upper .95
## intercept 261.657
## arm 1.138
## TYPESTATUSnumeric 1.301
## day 1.139
## earlyacademicyear 1.364
## white 1.192
## structuraletiology 1.402
## priorepilepsy 1.345
## status 1.367
## ageyears 1.038
## SEXnumeric 1.319
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.051 0.415 9.754 0.000 57.436 25.451
## arm 0.305 0.304 1.002 0.316 1.357 0.747
## TYPESTATUSnumeric 0.082 0.262 0.313 0.755 1.085 0.649
## day 0.332 0.315 1.054 0.292 1.394 0.752
## earlyacademicyear -0.080 0.305 -0.264 0.792 0.923 0.508
## white 0.225 0.334 0.674 0.500 1.253 0.651
## structuraletiology -0.042 0.337 -0.126 0.900 0.958 0.495
## priorepilepsy 0.120 0.405 0.296 0.768 1.127 0.509
## status 0.162 0.367 0.443 0.658 1.176 0.573
## ageyears -0.044 0.027 -1.622 0.105 0.957 0.908
## SEXnumeric 0.005 0.322 0.017 0.987 1.005 0.535
## upper .95
## intercept 129.617
## arm 2.463
## TYPESTATUSnumeric 1.815
## day 2.583
## earlyacademicyear 1.676
## white 2.412
## structuraletiology 1.854
## priorepilepsy 2.494
## status 2.413
## ageyears 1.009
## SEXnumeric 1.891
Time to treatment sensitivity analysis 2: Only initial centers
# Reduce the database to only centers already in pSERG during 2011-2013
pSERG$center[grepl("Baylor", pSERG$PATIENT_LABEL)] <- "Bay"
pSERG$center[grepl("BCCH", pSERG$PATIENT_LABEL)] <- "BCCH"
pSERG$center[grepl("CCHMC", pSERG$PATIENT_LABEL)] <- "CCHMC"
pSERG$center[grepl("CHB", pSERG$PATIENT_LABEL)] <- "CHB"
pSERG$center[grepl("Chicago", pSERG$PATIENT_LABEL)] <- "Chicago"
pSERG$center[grepl("CHOP", pSERG$PATIENT_LABEL)] <- "CHOP"
pSERG$center[grepl("CNMC", pSERG$PATIENT_LABEL)] <- "CNMC"
pSERG$center[grepl("Colorado", pSERG$PATIENT_LABEL)] <- "Colorado"
pSERG$center[grepl("Duke", pSERG$PATIENT_LABEL)] <- "Duke"
pSERG$center[grepl("Mayo", pSERG$PATIENT_LABEL)] <- "Mayo"
pSERG$center[grepl("MCW", pSERG$PATIENT_LABEL)] <- "MCW"
pSERG$center[grepl("NCH", pSERG$PATIENT_LABEL)] <- "NCH"
pSERG$center[grepl("NYU", pSERG$PATIENT_LABEL)] <- "NYU"
pSERG$center[grepl("OHSU", pSERG$PATIENT_LABEL)] <- "OHSU"
pSERG$center[grepl("Phoenix", pSERG$PATIENT_LABEL)] <- "Phoenix"
pSERG$center[grepl("Seattle", pSERG$PATIENT_LABEL)] <- "Seattle"
pSERG$center[grepl("UVA", pSERG$PATIENT_LABEL)] <- "UVA"
pSERG$center[grepl("WUSTL", pSERG$PATIENT_LABEL)] <- "WUSTL"
pSERG <- pSERG[pSERG$center %in% unique(pSERG[pSERG$yearSE==2011 | pSERG$yearSE==2012 | pSERG$yearSE==2013, ]$center), ]
# Awareness in this new dataset
CrossTable(pSERG$awareness)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 150 | 118 |
## | 0.560 | 0.440 |
## |-----------|-----------|
##
##
##
##
## ALL PATIENTS
# Time to first BZD
summary(pSERG$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 20.00 56.67 45.00 1264.00
sd(pSERG$BZDTIME.0)
## [1] 134.0915
survfit(Surv(pSERG$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$BZDTIME.0) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## 268 268 20 15 23
# Figure time to first BZD
plot(survfit(Surv(pSERG$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$awareness == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 20.00 55.63 53.75 720.00
summary(pSERG[which(pSERG$awareness == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 19.00 57.99 38.00 1264.00
survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho = 1)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 150 76.5 80.6 0.207 0.763
## pSERG$awareness=1 118 63.2 59.1 0.283 0.763
##
## Chisq= 0.8 on 1 degrees of freedom, p= 0.4
pchisq(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.3824629
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$BZDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS +
## pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 268, number of events= 268
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness 0.071287 1.073889 0.126344 0.564 0.572596
## pSERG$TYPESTATUSintermittent -0.386844 0.679197 0.139448 -2.774 0.005535
## pSERG$HOSPITALONSETyes 0.534269 1.706201 0.141619 3.773 0.000162
## pSERG$day 0.068962 1.071396 0.127855 0.539 0.589626
## pSERG$earlyacademicyear 0.225338 1.252746 0.124845 1.805 0.071083
## pSERG$white 0.088748 1.092805 0.132925 0.668 0.504354
## pSERG$structuraletiology 0.057031 1.058688 0.147905 0.386 0.699799
## pSERG$priorepilepsy 0.026083 1.026427 0.138248 0.189 0.850351
## pSERG$status 0.373708 1.453113 0.173984 2.148 0.031718
## pSERG$ageyears -0.003736 0.996271 0.012386 -0.302 0.762929
## pSERG$SEXmale 0.066437 1.068694 0.127616 0.521 0.602644
##
## pSERG$awareness
## pSERG$TYPESTATUSintermittent **
## pSERG$HOSPITALONSETyes ***
## pSERG$day
## pSERG$earlyacademicyear .
## pSERG$white
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status *
## pSERG$ageyears
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness 1.0739 0.9312 0.8383 1.3756
## pSERG$TYPESTATUSintermittent 0.6792 1.4723 0.5168 0.8927
## pSERG$HOSPITALONSETyes 1.7062 0.5861 1.2927 2.2520
## pSERG$day 1.0714 0.9334 0.8339 1.3765
## pSERG$earlyacademicyear 1.2527 0.7982 0.9808 1.6000
## pSERG$white 1.0928 0.9151 0.8422 1.4180
## pSERG$structuraletiology 1.0587 0.9446 0.7923 1.4147
## pSERG$priorepilepsy 1.0264 0.9743 0.7828 1.3459
## pSERG$status 1.4531 0.6882 1.0332 2.0436
## pSERG$ageyears 0.9963 1.0037 0.9724 1.0208
## pSERG$SEXmale 1.0687 0.9357 0.8322 1.3724
##
## Concordance= 0.618 (se = 0.022 )
## Rsquare= 0.117 (max possible= 1 )
## Likelihood ratio test= 33.22 on 11 df, p=5e-04
## Wald test = 34.76 on 11 df, p=3e-04
## Score (logrank) test = 35.44 on 11 df, p=2e-04
# Time to first non-BZD AED
summary(pSERG$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 35.0 65.5 153.2 150.8 1800.0
sd(pSERG$AEDTIME.0)
## [1] 246.9072
survfit(Surv(pSERG$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$AEDTIME.0) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## 268.0 268.0 65.5 60.0 77.0
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$awareness == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 34.25 66.50 154.97 160.00 1800.00
summary(pSERG[which(pSERG$awareness == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 38.5 65.5 151.0 149.8 1419.0
survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho = 1)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 150 76.0 75.7 0.00127 0.00433
## pSERG$awareness=1 118 59.5 59.8 0.00160 0.00433
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
pchisq(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.9475229
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$AEDTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS +
## pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 268, number of events= 268
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness 0.01401 1.01411 0.12583 0.111 0.9113
## pSERG$TYPESTATUSintermittent -0.55876 0.57192 0.13948 -4.006 6.17e-05
## pSERG$HOSPITALONSETyes 0.86300 2.37026 0.14291 6.039 1.55e-09
## pSERG$day 0.25802 1.29436 0.13054 1.977 0.0481
## pSERG$earlyacademicyear 0.11097 1.11736 0.12516 0.887 0.3753
## pSERG$white 0.02929 1.02972 0.12918 0.227 0.8207
## pSERG$structuraletiology 0.18535 1.20364 0.14530 1.276 0.2021
## pSERG$priorepilepsy 0.09298 1.09744 0.14163 0.657 0.5115
## pSERG$status 0.20088 1.22247 0.17296 1.161 0.2455
## pSERG$ageyears -0.02759 0.97279 0.01223 -2.256 0.0241
## pSERG$SEXmale 0.04745 1.04859 0.12977 0.366 0.7146
##
## pSERG$awareness
## pSERG$TYPESTATUSintermittent ***
## pSERG$HOSPITALONSETyes ***
## pSERG$day *
## pSERG$earlyacademicyear
## pSERG$white
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status
## pSERG$ageyears *
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness 1.0141 0.9861 0.7925 1.2978
## pSERG$TYPESTATUSintermittent 0.5719 1.7485 0.4351 0.7517
## pSERG$HOSPITALONSETyes 2.3703 0.4219 1.7912 3.1364
## pSERG$day 1.2944 0.7726 1.0022 1.6718
## pSERG$earlyacademicyear 1.1174 0.8950 0.8743 1.4280
## pSERG$white 1.0297 0.9711 0.7994 1.3264
## pSERG$structuraletiology 1.2036 0.8308 0.9054 1.6002
## pSERG$priorepilepsy 1.0974 0.9112 0.8314 1.4486
## pSERG$status 1.2225 0.8180 0.8710 1.7158
## pSERG$ageyears 0.9728 1.0280 0.9497 0.9964
## pSERG$SEXmale 1.0486 0.9537 0.8131 1.3523
##
## Concordance= 0.647 (se = 0.021 )
## Rsquare= 0.191 (max possible= 1 )
## Likelihood ratio test= 56.88 on 11 df, p=4e-08
## Wald test = 58.87 on 11 df, p=2e-08
## Score (logrank) test = 60.39 on 11 df, p=8e-09
# Time to first CI
summary(pSERG$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 122.0 206.0 523.7 612.5 7200.0 149
sd(pSERG$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$CONTTIME.0) ~ 1)
##
## 149 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 119 119 206 165 300
# Figure time to first CI
plot(survfit(Surv(pSERG$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$awareness == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 123.5 180.0 496.2 539.0 4320.0 83
summary(pSERG[which(pSERG$awareness == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 45.0 122.0 212.0 559.1 660.5 7200.0 66
survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$CONTTIME.0) ~ pSERG$awareness,
## rho = 1)
##
## n=119, 149 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 67 34.5 33.3 0.0440 0.148
## pSERG$awareness=1 52 25.7 26.9 0.0544 0.148
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
pchisq(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.7006702
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$CONTTIME.0) ~ pSERG$awareness + pSERG$TYPESTATUS +
## pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 119, number of events= 119
## (149 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness -0.026718 0.973636 0.197159 -0.136 0.8922
## pSERG$TYPESTATUSintermittent -0.221570 0.801260 0.226611 -0.978 0.3282
## pSERG$HOSPITALONSETyes 0.089413 1.093533 0.224921 0.398 0.6910
## pSERG$day 0.016079 1.016209 0.195058 0.082 0.9343
## pSERG$earlyacademicyear 0.477221 1.611589 0.203899 2.340 0.0193
## pSERG$white -0.484381 0.616078 0.216217 -2.240 0.0251
## pSERG$structuraletiology 0.161824 1.175653 0.238960 0.677 0.4983
## pSERG$priorepilepsy 0.198720 1.219841 0.242792 0.818 0.4131
## pSERG$status 0.095529 1.100241 0.274854 0.348 0.7282
## pSERG$ageyears -0.001423 0.998578 0.019819 -0.072 0.9427
## pSERG$SEXmale 0.360315 1.433781 0.201243 1.790 0.0734
##
## pSERG$awareness
## pSERG$TYPESTATUSintermittent
## pSERG$HOSPITALONSETyes
## pSERG$day
## pSERG$earlyacademicyear *
## pSERG$white *
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status
## pSERG$ageyears
## pSERG$SEXmale .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness 0.9736 1.0271 0.6616 1.4329
## pSERG$TYPESTATUSintermittent 0.8013 1.2480 0.5139 1.2493
## pSERG$HOSPITALONSETyes 1.0935 0.9145 0.7037 1.6994
## pSERG$day 1.0162 0.9841 0.6933 1.4894
## pSERG$earlyacademicyear 1.6116 0.6205 1.0807 2.4033
## pSERG$white 0.6161 1.6232 0.4033 0.9412
## pSERG$structuraletiology 1.1757 0.8506 0.7360 1.8779
## pSERG$priorepilepsy 1.2198 0.8198 0.7579 1.9632
## pSERG$status 1.1002 0.9089 0.6420 1.8856
## pSERG$ageyears 0.9986 1.0014 0.9605 1.0381
## pSERG$SEXmale 1.4338 0.6975 0.9665 2.1271
##
## Concordance= 0.586 (se = 0.031 )
## Rsquare= 0.115 (max possible= 1 )
## Likelihood ratio test= 14.5 on 11 df, p=0.2
## Wald test = 14.42 on 11 df, p=0.2
## Score (logrank) test = 14.53 on 11 df, p=0.2
# First BZD later than 20 minutes
CrossTable(pSERG$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 149 | 119 |
## | 0.556 | 0.444 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 150
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 81 | 69 |
## | 0.540 | 0.460 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 118
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 68 | 50 |
## | 0.576 | 0.424 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore20min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore20min and pSERG$awareness
## p-value = 0.6206
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5152177 1.4441696
## sample estimates:
## odds ratio
## 0.8636474
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=20,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.016 -1.692 1.723 0.986
## RMST (arm=1)/(arm=0) 1.003 0.884 1.137 0.968
## RMTL (arm=1)/(arm=0) 1.004 0.767 1.313 0.979
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 16.337 1.375 11.879 0.000 13.642 19.033
## arm 0.016 0.871 0.018 0.986 -1.692 1.723
## TYPESTATUSnumeric -0.473 0.902 -0.524 0.600 -2.242 1.296
## HOSPITALONSETnumeric -3.496 0.982 -3.560 0.000 -5.420 -1.571
## day -0.628 0.855 -0.734 0.463 -2.304 1.048
## earlyacademicyear -0.951 0.864 -1.101 0.271 -2.644 0.742
## white -0.248 0.874 -0.283 0.777 -1.960 1.465
## structuraletiology -0.499 1.015 -0.492 0.623 -2.488 1.490
## priorepilepsy -0.412 0.896 -0.459 0.646 -2.169 1.345
## status -2.789 1.271 -2.195 0.028 -5.280 -0.299
## ageyears 0.010 0.089 0.108 0.914 -0.164 0.183
## SEXnumeric 0.553 0.874 0.633 0.527 -1.160 2.267
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.809 0.100 27.986 0.000 16.589 13.627
## arm 0.003 0.064 0.040 0.968 1.003 0.884
## TYPESTATUSnumeric -0.032 0.067 -0.480 0.631 0.968 0.849
## HOSPITALONSETnumeric -0.275 0.082 -3.343 0.001 0.760 0.646
## day -0.049 0.062 -0.789 0.430 0.952 0.842
## earlyacademicyear -0.071 0.063 -1.116 0.264 0.932 0.823
## white -0.020 0.064 -0.313 0.755 0.980 0.864
## structuraletiology -0.037 0.076 -0.492 0.623 0.963 0.830
## priorepilepsy -0.034 0.064 -0.531 0.595 0.967 0.853
## status -0.225 0.108 -2.084 0.037 0.798 0.646
## ageyears 0.001 0.006 0.131 0.895 1.001 0.988
## SEXnumeric 0.041 0.064 0.643 0.520 1.042 0.919
## upper .95
## intercept 20.196
## arm 1.137
## TYPESTATUSnumeric 1.104
## HOSPITALONSETnumeric 0.892
## day 1.076
## earlyacademicyear 1.055
## white 1.111
## structuraletiology 1.118
## priorepilepsy 1.096
## status 0.987
## ageyears 1.014
## SEXnumeric 1.182
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.411 0.226 6.254 0.000 4.099 2.634
## arm 0.004 0.137 0.026 0.979 1.004 0.767
## TYPESTATUSnumeric 0.089 0.140 0.632 0.528 1.093 0.830
## HOSPITALONSETnumeric 0.499 0.139 3.588 0.000 1.647 1.254
## day 0.085 0.138 0.612 0.540 1.088 0.830
## earlyacademicyear 0.146 0.139 1.057 0.290 1.158 0.882
## white 0.031 0.138 0.225 0.822 1.032 0.786
## structuraletiology 0.075 0.155 0.487 0.627 1.078 0.796
## priorepilepsy 0.048 0.153 0.312 0.755 1.049 0.777
## status 0.379 0.170 2.222 0.026 1.461 1.046
## ageyears -0.001 0.014 -0.056 0.955 0.999 0.972
## SEXnumeric -0.085 0.140 -0.607 0.544 0.918 0.698
## upper .95
## intercept 6.378
## arm 1.313
## TYPESTATUSnumeric 1.439
## HOSPITALONSETnumeric 2.164
## day 1.427
## earlyacademicyear 1.519
## white 1.353
## structuraletiology 1.459
## priorepilepsy 1.417
## status 2.040
## ageyears 1.027
## SEXnumeric 1.209
# First BZD later than 40 minutes
CrossTable(pSERG$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 192 | 76 |
## | 0.716 | 0.284 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 150
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 103 | 47 |
## | 0.687 | 0.313 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 118
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 89 | 29 |
## | 0.754 | 0.246 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore40min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore40min and pSERG$awareness
## p-value = 0.2748
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3982146 1.2693869
## sample estimates:
## odds ratio
## 0.7149781
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=40,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.243 -4.736 2.250 0.486
## RMST (arm=1)/(arm=0) 0.947 0.799 1.124 0.535
## RMTL (arm=1)/(arm=0) 1.074 0.897 1.286 0.437
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 26.287 2.750 9.560 0.000 20.898 31.677
## arm -1.243 1.782 -0.697 0.486 -4.736 2.250
## TYPESTATUSnumeric -3.584 1.749 -2.049 0.040 -7.013 -0.155
## HOSPITALONSETnumeric -7.759 1.851 -4.193 0.000 -11.386 -4.132
## day -0.530 1.778 -0.298 0.766 -4.014 2.954
## earlyacademicyear -1.998 1.753 -1.140 0.254 -5.434 1.437
## white 0.511 1.797 0.285 0.776 -3.011 4.033
## structuraletiology 0.484 2.095 0.231 0.817 -3.623 4.590
## priorepilepsy 0.713 1.874 0.380 0.704 -2.961 4.386
## status -5.873 2.455 -2.392 0.017 -10.685 -1.061
## ageyears 0.052 0.177 0.294 0.769 -0.295 0.399
## SEXnumeric -0.163 1.772 -0.092 0.927 -3.636 3.310
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.287 0.131 25.126 0.000 26.757 20.706
## arm -0.054 0.087 -0.620 0.535 0.947 0.799
## TYPESTATUSnumeric -0.171 0.089 -1.921 0.055 0.843 0.709
## HOSPITALONSETnumeric -0.404 0.107 -3.795 0.000 0.667 0.542
## day -0.030 0.085 -0.349 0.727 0.971 0.822
## earlyacademicyear -0.097 0.085 -1.142 0.253 0.908 0.769
## white 0.023 0.087 0.264 0.792 1.023 0.862
## structuraletiology 0.024 0.100 0.243 0.808 1.025 0.842
## priorepilepsy 0.025 0.086 0.295 0.768 1.026 0.866
## status -0.310 0.143 -2.160 0.031 0.734 0.554
## ageyears 0.003 0.008 0.305 0.760 1.003 0.986
## SEXnumeric -0.004 0.085 -0.048 0.962 0.996 0.844
## upper .95
## intercept 34.577
## arm 1.124
## TYPESTATUSnumeric 1.003
## HOSPITALONSETnumeric 0.822
## day 1.146
## earlyacademicyear 1.072
## white 1.214
## structuraletiology 1.247
## priorepilepsy 1.215
## status 0.972
## ageyears 1.019
## SEXnumeric 1.176
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.652 0.149 17.754 0.000 14.180 10.581
## arm 0.071 0.092 0.777 0.437 1.074 0.897
## TYPESTATUSnumeric 0.191 0.089 2.148 0.032 1.210 1.017
## HOSPITALONSETnumeric 0.384 0.091 4.235 0.000 1.468 1.229
## day 0.023 0.095 0.246 0.806 1.024 0.850
## earlyacademicyear 0.104 0.092 1.129 0.259 1.110 0.926
## white -0.029 0.094 -0.310 0.756 0.971 0.808
## structuraletiology -0.024 0.111 -0.221 0.825 0.976 0.786
## priorepilepsy -0.048 0.105 -0.459 0.646 0.953 0.777
## status 0.289 0.116 2.499 0.012 1.336 1.064
## ageyears -0.003 0.009 -0.281 0.779 0.997 0.979
## SEXnumeric 0.011 0.094 0.121 0.903 1.011 0.842
## upper .95
## intercept 19.003
## arm 1.286
## TYPESTATUSnumeric 1.441
## HOSPITALONSETnumeric 1.753
## day 1.233
## earlyacademicyear 1.330
## white 1.167
## structuraletiology 1.212
## priorepilepsy 1.170
## status 1.676
## ageyears 1.016
## SEXnumeric 1.215
# First BZD later than 60 minutes
CrossTable(pSERG$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 217 | 51 |
## | 0.810 | 0.190 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 150
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 116 | 34 |
## | 0.773 | 0.227 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 118
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 101 | 17 |
## | 0.856 | 0.144 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore60min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore60min and pSERG$awareness
## p-value = 0.1165
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2833536 1.1330544
## sample estimates:
## odds ratio
## 0.5754247
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=60,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.652 -7.679 2.376 0.301
## RMST (arm=1)/(arm=0) 0.910 0.743 1.114 0.361
## RMTL (arm=1)/(arm=0) 1.086 0.939 1.255 0.265
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.954 4.016 8.703 0.000 27.082 42.826
## arm -2.652 2.565 -1.034 0.301 -7.679 2.376
## TYPESTATUSnumeric -6.440 2.506 -2.570 0.010 -11.352 -1.528
## HOSPITALONSETnumeric -10.710 2.613 -4.100 0.000 -15.831 -5.590
## day -0.646 2.599 -0.249 0.804 -5.740 4.448
## earlyacademicyear -3.445 2.542 -1.355 0.175 -8.426 1.537
## white 0.064 2.648 0.024 0.981 -5.126 5.254
## structuraletiology 0.762 3.002 0.254 0.800 -5.122 6.646
## priorepilepsy 2.432 2.745 0.886 0.376 -2.948 7.812
## status -9.665 3.267 -2.958 0.003 -16.068 -3.262
## ageyears 0.045 0.253 0.177 0.859 -0.451 0.541
## SEXnumeric -1.249 2.566 -0.487 0.627 -6.279 3.781
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.588 0.152 23.580 0.000 36.166 26.840
## arm -0.094 0.103 -0.913 0.361 0.910 0.743
## TYPESTATUSnumeric -0.253 0.106 -2.381 0.017 0.776 0.631
## HOSPITALONSETnumeric -0.457 0.125 -3.663 0.000 0.633 0.496
## day -0.032 0.100 -0.314 0.754 0.969 0.796
## earlyacademicyear -0.134 0.100 -1.342 0.180 0.874 0.719
## white 0.002 0.104 0.015 0.988 1.002 0.817
## structuraletiology 0.030 0.116 0.256 0.798 1.030 0.821
## priorepilepsy 0.081 0.102 0.795 0.427 1.084 0.888
## status -0.421 0.163 -2.589 0.010 0.656 0.477
## ageyears 0.002 0.010 0.164 0.870 1.002 0.983
## SEXnumeric -0.043 0.099 -0.430 0.667 0.958 0.789
## upper .95
## intercept 48.734
## arm 1.114
## TYPESTATUSnumeric 0.956
## HOSPITALONSETnumeric 0.809
## day 1.180
## earlyacademicyear 1.064
## white 1.228
## structuraletiology 1.292
## priorepilepsy 1.323
## status 0.903
## ageyears 1.021
## SEXnumeric 1.164
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.253 0.123 26.439 0.000 25.857 20.317
## arm 0.082 0.074 1.115 0.265 1.086 0.939
## TYPESTATUSnumeric 0.189 0.071 2.641 0.008 1.208 1.050
## HOSPITALONSETnumeric 0.300 0.073 4.128 0.000 1.350 1.171
## day 0.016 0.077 0.202 0.840 1.016 0.873
## earlyacademicyear 0.101 0.075 1.354 0.176 1.106 0.956
## white -0.003 0.077 -0.037 0.970 0.997 0.857
## structuraletiology -0.022 0.089 -0.253 0.800 0.978 0.822
## priorepilepsy -0.079 0.085 -0.935 0.350 0.924 0.782
## status 0.270 0.089 3.035 0.002 1.310 1.100
## ageyears -0.001 0.008 -0.184 0.854 0.999 0.984
## SEXnumeric 0.039 0.076 0.509 0.611 1.039 0.896
## upper .95
## intercept 32.907
## arm 1.255
## TYPESTATUSnumeric 1.389
## HOSPITALONSETnumeric 1.556
## day 1.182
## earlyacademicyear 1.281
## white 1.161
## structuraletiology 1.164
## priorepilepsy 1.091
## status 1.560
## ageyears 1.013
## SEXnumeric 1.206
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 76 | 192 |
## | 0.284 | 0.716 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 150
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 45 | 105 |
## | 0.300 | 0.700 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 118
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 31 | 87 |
## | 0.263 | 0.737 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore40min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore40min and pSERG$awareness
## p-value = 0.5853
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6792881 2.1444062
## sample estimates:
## odds ratio
## 1.201938
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=40,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.661 -2.779 1.457 0.541
## RMST (arm=1)/(arm=0) 0.982 0.924 1.043 0.558
## RMTL (arm=1)/(arm=0) 1.199 0.772 1.861 0.419
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 35.105 1.871 18.764 0.000 31.438 38.772
## arm -0.661 1.081 -0.612 0.541 -2.779 1.457
## TYPESTATUSnumeric -0.081 1.117 -0.073 0.942 -2.270 2.107
## HOSPITALONSETnumeric -6.909 1.394 -4.955 0.000 -9.642 -4.176
## day -1.442 1.114 -1.294 0.196 -3.626 0.742
## earlyacademicyear 0.864 1.076 0.803 0.422 -1.244 2.972
## white 1.814 1.097 1.653 0.098 -0.336 3.964
## structuraletiology -0.302 1.186 -0.255 0.799 -2.626 2.022
## priorepilepsy 1.262 1.111 1.136 0.256 -0.915 3.439
## status -0.599 1.361 -0.441 0.659 -3.266 2.067
## ageyears 0.169 0.111 1.519 0.129 -0.049 0.387
## SEXnumeric 0.844 1.136 0.743 0.458 -1.383 3.070
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.555 0.054 65.491 0.000 34.985 31.454
## arm -0.018 0.031 -0.586 0.558 0.982 0.924
## TYPESTATUSnumeric -0.002 0.032 -0.057 0.955 0.998 0.938
## HOSPITALONSETnumeric -0.204 0.044 -4.603 0.000 0.815 0.747
## day -0.040 0.032 -1.255 0.209 0.961 0.903
## earlyacademicyear 0.024 0.031 0.781 0.435 1.024 0.964
## white 0.051 0.032 1.613 0.107 1.053 0.989
## structuraletiology -0.008 0.034 -0.231 0.817 0.992 0.927
## priorepilepsy 0.035 0.031 1.124 0.261 1.036 0.974
## status -0.016 0.039 -0.416 0.678 0.984 0.912
## ageyears 0.005 0.003 1.517 0.129 1.005 0.999
## SEXnumeric 0.024 0.032 0.734 0.463 1.024 0.961
## upper .95
## intercept 38.913
## arm 1.043
## TYPESTATUSnumeric 1.062
## HOSPITALONSETnumeric 0.889
## day 1.023
## earlyacademicyear 1.088
## white 1.120
## structuraletiology 1.061
## priorepilepsy 1.102
## status 1.062
## ageyears 1.011
## SEXnumeric 1.091
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.373 0.385 3.564 0.000 3.948 1.855
## arm 0.181 0.224 0.808 0.419 1.199 0.772
## TYPESTATUSnumeric 0.056 0.253 0.220 0.826 1.057 0.643
## HOSPITALONSETnumeric 1.309 0.248 5.281 0.000 3.704 2.278
## day 0.376 0.250 1.499 0.134 1.456 0.891
## earlyacademicyear -0.216 0.225 -0.958 0.338 0.806 0.519
## white -0.417 0.217 -1.922 0.055 0.659 0.431
## structuraletiology 0.094 0.223 0.422 0.673 1.099 0.709
## priorepilepsy -0.313 0.276 -1.136 0.256 0.731 0.426
## status 0.181 0.308 0.587 0.557 1.198 0.655
## ageyears -0.040 0.027 -1.489 0.136 0.961 0.911
## SEXnumeric -0.225 0.249 -0.904 0.366 0.799 0.491
## upper .95
## intercept 8.403
## arm 1.861
## TYPESTATUSnumeric 1.737
## HOSPITALONSETnumeric 6.022
## day 2.379
## earlyacademicyear 1.253
## white 1.008
## structuraletiology 1.703
## priorepilepsy 1.255
## status 2.193
## ageyears 1.013
## SEXnumeric 1.300
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 120 | 148 |
## | 0.448 | 0.552 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 150
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 70 | 80 |
## | 0.467 | 0.533 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 118
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 50 | 68 |
## | 0.424 | 0.576 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore60min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore60min and pSERG$awareness
## p-value = 0.5366
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.7114192 1.9931234
## sample estimates:
## odds ratio
## 1.189223
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=60,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.253 -3.504 4.009 0.895
## RMST (arm=1)/(arm=0) 1.007 0.931 1.089 0.863
## RMTL (arm=1)/(arm=0) 1.007 0.728 1.394 0.966
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 48.381 3.350 14.441 0.000 41.814 54.947
## arm 0.253 1.917 0.132 0.895 -3.504 4.009
## TYPESTATUSnumeric -1.715 2.009 -0.853 0.393 -5.652 2.223
## HOSPITALONSETnumeric -13.584 2.409 -5.638 0.000 -18.306 -8.862
## day -3.253 1.966 -1.654 0.098 -7.107 0.602
## earlyacademicyear 2.209 1.925 1.147 0.251 -1.565 5.983
## white 2.464 1.986 1.241 0.215 -1.428 6.355
## structuraletiology -1.778 2.163 -0.822 0.411 -6.018 2.462
## priorepilepsy 2.555 2.008 1.272 0.203 -1.381 6.490
## status -0.993 2.353 -0.422 0.673 -5.604 3.618
## ageyears 0.327 0.197 1.662 0.097 -0.059 0.713
## SEXnumeric 2.367 1.995 1.186 0.235 -1.543 6.277
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.873 0.072 54.079 0.000 48.092 41.794
## arm 0.007 0.040 0.173 0.863 1.007 0.931
## TYPESTATUSnumeric -0.035 0.042 -0.836 0.403 0.966 0.889
## HOSPITALONSETnumeric -0.299 0.059 -5.090 0.000 0.741 0.661
## day -0.066 0.041 -1.614 0.106 0.936 0.864
## earlyacademicyear 0.044 0.040 1.098 0.272 1.045 0.966
## white 0.050 0.042 1.183 0.237 1.051 0.968
## structuraletiology -0.036 0.047 -0.769 0.442 0.965 0.880
## priorepilepsy 0.052 0.041 1.261 0.207 1.054 0.972
## status -0.019 0.049 -0.382 0.703 0.982 0.892
## ageyears 0.007 0.004 1.669 0.095 1.007 0.999
## SEXnumeric 0.048 0.042 1.156 0.248 1.049 0.967
## upper .95
## intercept 55.340
## arm 1.089
## TYPESTATUSnumeric 1.048
## HOSPITALONSETnumeric 0.832
## day 1.014
## earlyacademicyear 1.131
## white 1.142
## structuraletiology 1.057
## priorepilepsy 1.142
## status 1.080
## ageyears 1.015
## SEXnumeric 1.138
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.337 0.275 8.505 0.000 10.349 6.040
## arm 0.007 0.166 0.043 0.966 1.007 0.728
## TYPESTATUSnumeric 0.161 0.175 0.917 0.359 1.175 0.833
## HOSPITALONSETnumeric 1.043 0.175 5.960 0.000 2.838 2.014
## day 0.313 0.180 1.741 0.082 1.367 0.961
## earlyacademicyear -0.220 0.166 -1.324 0.186 0.802 0.579
## white -0.244 0.162 -1.511 0.131 0.783 0.571
## structuraletiology 0.168 0.164 1.024 0.306 1.183 0.857
## priorepilepsy -0.245 0.194 -1.265 0.206 0.783 0.535
## status 0.122 0.216 0.565 0.572 1.130 0.740
## ageyears -0.030 0.019 -1.578 0.115 0.970 0.935
## SEXnumeric -0.237 0.176 -1.345 0.179 0.789 0.558
## upper .95
## intercept 17.732
## arm 1.394
## TYPESTATUSnumeric 1.657
## HOSPITALONSETnumeric 4.000
## day 1.944
## earlyacademicyear 1.112
## white 1.075
## structuraletiology 1.633
## priorepilepsy 1.144
## status 1.725
## ageyears 1.007
## SEXnumeric 1.115
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 187 | 81 |
## | 0.698 | 0.302 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 150
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 105 | 45 |
## | 0.700 | 0.300 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 118
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 82 | 36 |
## | 0.695 | 0.305 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore120min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore120min and pSERG$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5845118 1.7884137
## sample estimates:
## odds ratio
## 1.024307
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness, tau=120,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.151 -8.971 8.669 0.973
## RMST (arm=1)/(arm=0) 1.005 0.890 1.135 0.934
## RMTL (arm=1)/(arm=0) 1.021 0.844 1.236 0.829
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 82.622 7.379 11.196 0.000 68.159 97.085
## arm -0.151 4.500 -0.034 0.973 -8.971 8.669
## TYPESTATUSnumeric -18.281 4.434 -4.123 0.000 -26.972 -9.590
## HOSPITALONSETnumeric -31.704 5.036 -6.295 0.000 -41.575 -21.833
## day -6.303 4.542 -1.388 0.165 -15.206 2.600
## earlyacademicyear 1.668 4.474 0.373 0.709 -7.101 10.436
## white 2.728 4.612 0.592 0.554 -6.311 11.767
## structuraletiology -6.339 5.141 -1.233 0.218 -16.415 3.736
## priorepilepsy 5.673 4.766 1.190 0.234 -3.668 15.014
## status -4.867 5.607 -0.868 0.385 -15.857 6.123
## ageyears 0.809 0.439 1.842 0.065 -0.052 1.670
## SEXnumeric 4.809 4.559 1.055 0.291 -4.126 13.744
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.411 0.103 42.711 0.000 82.370 67.275
## arm 0.005 0.062 0.083 0.934 1.005 0.890
## TYPESTATUSnumeric -0.260 0.065 -3.977 0.000 0.771 0.678
## HOSPITALONSETnumeric -0.477 0.085 -5.599 0.000 0.621 0.525
## day -0.086 0.062 -1.396 0.163 0.917 0.813
## earlyacademicyear 0.017 0.061 0.281 0.779 1.017 0.902
## white 0.029 0.064 0.453 0.650 1.030 0.907
## structuraletiology -0.085 0.076 -1.129 0.259 0.918 0.791
## priorepilepsy 0.074 0.064 1.161 0.246 1.077 0.950
## status -0.057 0.079 -0.727 0.467 0.944 0.809
## ageyears 0.011 0.006 1.859 0.063 1.011 0.999
## SEXnumeric 0.065 0.063 1.041 0.298 1.068 0.944
## upper .95
## intercept 100.852
## arm 1.135
## TYPESTATUSnumeric 0.876
## HOSPITALONSETnumeric 0.733
## day 1.035
## earlyacademicyear 1.147
## white 1.168
## structuraletiology 1.065
## priorepilepsy 1.220
## status 1.102
## ageyears 1.022
## SEXnumeric 1.208
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.614 0.159 22.680 0.000 37.118 27.161
## arm 0.021 0.097 0.215 0.829 1.021 0.844
## TYPESTATUSnumeric 0.377 0.094 4.026 0.000 1.458 1.213
## HOSPITALONSETnumeric 0.626 0.101 6.174 0.000 1.871 1.534
## day 0.135 0.102 1.325 0.185 1.145 0.937
## earlyacademicyear -0.049 0.098 -0.500 0.617 0.952 0.786
## white -0.081 0.097 -0.831 0.406 0.922 0.762
## structuraletiology 0.140 0.102 1.369 0.171 1.150 0.941
## priorepilepsy -0.134 0.111 -1.214 0.225 0.874 0.704
## status 0.129 0.121 1.067 0.286 1.138 0.898
## ageyears -0.018 0.011 -1.735 0.083 0.982 0.962
## SEXnumeric -0.110 0.101 -1.099 0.272 0.895 0.735
## upper .95
## intercept 50.726
## arm 1.236
## TYPESTATUSnumeric 1.751
## HOSPITALONSETnumeric 2.283
## day 1.398
## earlyacademicyear 1.153
## white 1.116
## structuraletiology 1.406
## priorepilepsy 1.086
## status 1.442
## ageyears 1.002
## SEXnumeric 1.090
# First CI later than 60 minutes
CrossTable(pSERG$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 119
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 110 |
## | 0.076 | 0.924 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 67
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 61 |
## | 0.090 | 0.910 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 52
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 49 |
## | 0.058 | 0.942 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstCImore60min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore60min and pSERG$awareness
## p-value = 0.7297
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3220185 10.3939712
## sample estimates:
## odds ratio
## 1.600393
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 2.045 -0.117 4.207 0.064
## RMST (arm=1)/(arm=0) 1.036 0.997 1.075 0.069
## RMTL (arm=1)/(arm=0) 0.136 0.029 0.641 0.012
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 59.038 1.645 35.895 0.000 55.814 62.262
## arm 2.045 1.103 1.854 0.064 -0.117 4.207
## TYPESTATUSnumeric 0.624 1.268 0.492 0.623 -1.862 3.109
## HOSPITALONSETnumeric -2.003 1.971 -1.016 0.310 -5.867 1.861
## day -1.289 1.111 -1.161 0.246 -3.466 0.888
## earlyacademicyear -1.200 1.562 -0.768 0.443 -4.261 1.862
## white -0.643 1.115 -0.576 0.564 -2.828 1.542
## structuraletiology 1.131 1.682 0.672 0.501 -2.166 4.427
## priorepilepsy -0.125 1.150 -0.109 0.914 -2.379 2.129
## status 1.975 1.019 1.939 0.053 -0.022 3.971
## ageyears 0.019 0.118 0.160 0.873 -0.212 0.249
## SEXnumeric -0.379 1.497 -0.254 0.800 -3.313 2.554
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.078 0.028 143.411 0.000 59.004 55.806
## arm 0.035 0.019 1.820 0.069 1.036 0.997
## TYPESTATUSnumeric 0.010 0.022 0.468 0.640 1.010 0.968
## HOSPITALONSETnumeric -0.035 0.035 -1.001 0.317 0.966 0.903
## day -0.022 0.019 -1.149 0.251 0.978 0.942
## earlyacademicyear -0.020 0.027 -0.761 0.447 0.980 0.930
## white -0.011 0.019 -0.565 0.572 0.989 0.953
## structuraletiology 0.020 0.029 0.678 0.498 1.020 0.963
## priorepilepsy -0.002 0.020 -0.101 0.920 0.998 0.960
## status 0.033 0.017 1.921 0.055 1.034 0.999
## ageyears 0.000 0.002 0.162 0.871 1.000 0.996
## SEXnumeric -0.006 0.026 -0.243 0.808 0.994 0.945
## upper .95
## intercept 62.386
## arm 1.075
## TYPESTATUSnumeric 1.054
## HOSPITALONSETnumeric 1.034
## day 1.016
## earlyacademicyear 1.033
## white 1.027
## structuraletiology 1.080
## priorepilepsy 1.037
## status 1.070
## ageyears 1.004
## SEXnumeric 1.045
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept -1.638 1.696 -0.966 0.334 0.194 0.007
## arm -1.994 0.790 -2.523 0.012 0.136 0.029
## TYPESTATUSnumeric -1.318 1.023 -1.289 0.197 0.268 0.036
## HOSPITALONSETnumeric 1.419 1.017 1.395 0.163 4.133 0.563
## day 0.932 0.836 1.115 0.265 2.539 0.493
## earlyacademicyear 1.310 0.781 1.677 0.094 3.707 0.801
## white 0.960 0.682 1.408 0.159 2.611 0.686
## structuraletiology -0.603 0.981 -0.615 0.539 0.547 0.080
## priorepilepsy -0.001 0.850 -0.002 0.999 0.999 0.189
## status -17.991 0.863 -20.848 0.000 0.000 0.000
## ageyears 0.010 0.063 0.162 0.872 1.010 0.894
## SEXnumeric 0.784 0.899 0.872 0.383 2.190 0.376
## upper .95
## intercept 5.398
## arm 0.641
## TYPESTATUSnumeric 1.987
## HOSPITALONSETnumeric 30.364
## day 13.065
## earlyacademicyear 17.145
## white 9.930
## structuraletiology 3.743
## priorepilepsy 5.285
## status 0.000
## ageyears 1.142
## SEXnumeric 12.748
# First CI later than 120 minutes
CrossTable(pSERG$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 119
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 91 |
## | 0.235 | 0.765 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 67
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 16 | 51 |
## | 0.239 | 0.761 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 52
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 12 | 40 |
## | 0.231 | 0.769 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstCImore120min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore120min and pSERG$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4100271 2.7213761
## sample estimates:
## odds ratio
## 1.045373
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 3.038 -4.854 10.930 0.451
## RMST (arm=1)/(arm=0) 1.027 0.955 1.104 0.468
## RMTL (arm=1)/(arm=0) 0.642 0.285 1.445 0.284
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 111.363 6.653 16.738 0.000 98.323 124.404
## arm 3.038 4.026 0.755 0.451 -4.854 10.930
## TYPESTATUSnumeric 1.698 4.637 0.366 0.714 -7.391 10.786
## HOSPITALONSETnumeric 1.582 5.099 0.310 0.756 -8.412 11.575
## day -6.270 4.031 -1.556 0.120 -14.170 1.629
## earlyacademicyear -6.810 4.839 -1.407 0.159 -16.295 2.675
## white 4.298 4.376 0.982 0.326 -4.278 12.874
## structuraletiology 1.510 5.509 0.274 0.784 -9.288 12.308
## priorepilepsy -2.934 4.747 -0.618 0.537 -12.238 6.370
## status 10.355 4.022 2.574 0.010 2.471 18.239
## ageyears 0.153 0.384 0.397 0.691 -0.601 0.906
## SEXnumeric -2.369 4.530 -0.523 0.601 -11.248 6.510
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.711 0.060 78.323 0.000 111.189 98.823
## arm 0.027 0.037 0.726 0.468 1.027 0.955
## TYPESTATUSnumeric 0.016 0.042 0.371 0.711 1.016 0.935
## HOSPITALONSETnumeric 0.014 0.046 0.312 0.755 1.014 0.927
## day -0.057 0.037 -1.546 0.122 0.944 0.878
## earlyacademicyear -0.061 0.044 -1.393 0.164 0.940 0.862
## white 0.039 0.040 0.979 0.328 1.040 0.961
## structuraletiology 0.014 0.050 0.273 0.785 1.014 0.919
## priorepilepsy -0.027 0.043 -0.611 0.541 0.974 0.894
## status 0.092 0.037 2.528 0.011 1.097 1.021
## ageyears 0.001 0.003 0.406 0.685 1.001 0.995
## SEXnumeric -0.021 0.041 -0.518 0.604 0.979 0.903
## upper .95
## intercept 125.101
## arm 1.104
## TYPESTATUSnumeric 1.103
## HOSPITALONSETnumeric 1.110
## day 1.015
## earlyacademicyear 1.025
## white 1.125
## structuraletiology 1.119
## priorepilepsy 1.060
## status 1.178
## ageyears 1.008
## SEXnumeric 1.061
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.885 0.846 2.229 0.026 6.585 1.255
## arm -0.444 0.414 -1.071 0.284 0.642 0.285
## TYPESTATUSnumeric -0.151 0.519 -0.292 0.771 0.859 0.311
## HOSPITALONSETnumeric -0.175 0.594 -0.295 0.768 0.839 0.262
## day 0.648 0.445 1.455 0.146 1.911 0.798
## earlyacademicyear 0.807 0.584 1.382 0.167 2.242 0.713
## white -0.412 0.457 -0.902 0.367 0.662 0.271
## structuraletiology -0.155 0.580 -0.267 0.789 0.856 0.275
## priorepilepsy 0.340 0.481 0.707 0.480 1.405 0.547
## status -1.652 0.756 -2.184 0.029 0.192 0.044
## ageyears -0.010 0.042 -0.247 0.805 0.990 0.911
## SEXnumeric 0.294 0.454 0.647 0.518 1.341 0.551
## upper .95
## intercept 34.545
## arm 1.445
## TYPESTATUSnumeric 2.379
## HOSPITALONSETnumeric 2.689
## day 4.576
## earlyacademicyear 7.046
## white 1.621
## structuraletiology 2.671
## priorepilepsy 3.610
## status 0.844
## ageyears 1.075
## SEXnumeric 3.264
# First CI later than 240 minutes
CrossTable(pSERG$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 119
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 68 | 51 |
## | 0.571 | 0.429 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 67
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 39 | 28 |
## | 0.582 | 0.418 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 52
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 29 | 23 |
## | 0.558 | 0.442 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstCImore240min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore240min and pSERG$awareness
## p-value = 0.8528
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.497905 2.444640
## sample estimates:
## odds ratio
## 1.103753
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 9.024 -14.478 32.525 0.452
## RMST (arm=1)/(arm=0) 1.052 0.921 1.201 0.456
## RMTL (arm=1)/(arm=0) 0.863 0.590 1.260 0.445
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 187.930 20.849 9.014 0.000 147.067 228.794
## arm 9.024 11.991 0.753 0.452 -14.478 32.525
## TYPESTATUSnumeric -19.455 13.594 -1.431 0.152 -46.100 7.189
## HOSPITALONSETnumeric 5.171 13.543 0.382 0.703 -21.373 31.716
## day -11.285 12.418 -0.909 0.363 -35.624 13.053
## earlyacademicyear -20.409 13.269 -1.538 0.124 -46.415 5.597
## white 13.185 13.067 1.009 0.313 -12.426 38.797
## structuraletiology 1.866 15.551 0.120 0.904 -28.613 32.346
## priorepilepsy 2.507 14.270 0.176 0.861 -25.462 30.475
## status 10.862 14.508 0.749 0.454 -17.574 39.297
## ageyears 0.148 1.197 0.124 0.901 -2.198 2.495
## SEXnumeric -12.159 12.430 -0.978 0.328 -36.520 12.203
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.232 0.115 45.488 0.000 187.115 149.352
## arm 0.051 0.068 0.746 0.456 1.052 0.921
## TYPESTATUSnumeric -0.113 0.081 -1.399 0.162 0.893 0.762
## HOSPITALONSETnumeric 0.027 0.075 0.358 0.720 1.027 0.887
## day -0.062 0.070 -0.889 0.374 0.940 0.820
## earlyacademicyear -0.114 0.075 -1.521 0.128 0.893 0.771
## white 0.075 0.075 0.990 0.322 1.077 0.930
## structuraletiology 0.010 0.087 0.115 0.908 1.010 0.852
## priorepilepsy 0.015 0.079 0.188 0.851 1.015 0.870
## status 0.063 0.080 0.785 0.433 1.065 0.910
## ageyears 0.001 0.007 0.135 0.893 1.001 0.988
## SEXnumeric -0.068 0.070 -0.974 0.330 0.934 0.814
## upper .95
## intercept 234.427
## arm 1.201
## TYPESTATUSnumeric 1.047
## HOSPITALONSETnumeric 1.190
## day 1.077
## earlyacademicyear 1.033
## white 1.249
## structuraletiology 1.197
## priorepilepsy 1.184
## status 1.246
## ageyears 1.014
## SEXnumeric 1.072
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.914 0.367 10.657 0.000 50.100 24.390
## arm -0.148 0.193 -0.764 0.445 0.863 0.590
## TYPESTATUSnumeric 0.290 0.196 1.484 0.138 1.337 0.911
## HOSPITALONSETnumeric -0.104 0.230 -0.454 0.650 0.901 0.574
## day 0.201 0.210 0.955 0.339 1.223 0.809
## earlyacademicyear 0.345 0.226 1.528 0.126 1.412 0.907
## white -0.212 0.203 -1.045 0.296 0.809 0.544
## structuraletiology -0.037 0.260 -0.142 0.887 0.964 0.579
## priorepilepsy -0.035 0.243 -0.143 0.886 0.966 0.600
## status -0.162 0.252 -0.644 0.520 0.850 0.519
## ageyears -0.002 0.021 -0.100 0.920 0.998 0.958
## SEXnumeric 0.196 0.202 0.973 0.330 1.217 0.820
## upper .95
## intercept 102.911
## arm 1.260
## TYPESTATUSnumeric 1.961
## HOSPITALONSETnumeric 1.414
## day 1.847
## earlyacademicyear 2.198
## white 1.204
## structuraletiology 1.604
## priorepilepsy 1.555
## status 1.393
## ageyears 1.039
## SEXnumeric 1.807
## OUT OF THE HOSPITAL
# At least one benzodiazepine before hospital arrival
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 134
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 51 | 83 |
## | 0.381 | 0.619 |
## |-----------|-----------|
##
##
##
##
# At least one benzodiazepine before hospital arrival depending on awareness
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 80
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 40 | 40 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 54
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 11 | 43 |
## | 0.204 | 0.796 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness)
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$AEDbeforehospital and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.0005647
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.669347 9.555395
## sample estimates:
## odds ratio
## 3.86888
# Logistic regression adjusting for potential confounders
logistic_out_of_hospital_BZD <- glm(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX, family="binomial")
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD), confint(logistic_out_of_hospital_BZD, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD))[ , 4])
## Waiting for profiling to be done...
## Odds ratio
## (Intercept) 2.5767264
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 5.2155487
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.3395691
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.0249173
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.0065879
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.5510430
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.9892032
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.7128893
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 6.7955604
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0353313
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7043755
## 2.5 %
## (Intercept) 0.6500875
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 2.1555797
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.1285737
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.4331861
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.4430720
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.2215725
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.3871155
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.3049884
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.8961407
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9571298
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.3042759
## 97.5 %
## (Intercept) 10.9981289
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 13.8314421
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.8390637
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 2.4254447
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.2823621
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.3148194
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 2.5645971
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.6351045
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 33.5950948
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.1244470
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.5975539
## p-value
## (Intercept) 0.186019439
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.000452268
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.022952796
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.955054417
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.987403978
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.186920250
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.981922387
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.426990492
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.007340804
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.393901759
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.404673944
# At least one benzodiazepine before hospital arrival among those with prior epilepsy
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 74
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 46 |
## | 0.378 | 0.622 |
## |-----------|-----------|
##
##
##
##
# At least one benzodiazepine before hospital arrival depending on awareness
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness == 0), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 42
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 22 |
## | 0.476 | 0.524 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness == 1), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 32
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 8 | 24 |
## | 0.250 | 0.750 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$awareness)
##
## Fisher's Exact Test for Count Data
##
## data:
## p-value = 0.05623
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.9071835 8.5977877
## sample estimates:
## odds ratio
## 2.690113
# Logistic regression adjusting for potential confounders among those with prior epilepsy
logistic_out_of_hospital_BZD_prior_epilepsy <- glm(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital ~ pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$awareness + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$day + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$white +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$structuraletiology +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$status + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$SEX, family="binomial")
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD_prior_epilepsy), confint(logistic_out_of_hospital_BZD_prior_epilepsy, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD_prior_epilepsy))[ , 4])
## Waiting for profiling to be done...
## Odds ratio
## (Intercept) 1.2905531
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness 3.0666397
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.3771600
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 1.4111264
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.5229581
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.5331197
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 1.4467025
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 9.7553032
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 1.0980402
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.8746648
## 2.5 %
## (Intercept) 0.16833418
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness 0.92332500
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.09430744
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 0.41364016
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.14710972
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.14354171
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 0.39412258
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 1.93487008
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 0.97526688
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.25369653
## 97.5 %
## (Intercept) 10.678022
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness 11.594062
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 1.338641
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 4.917801
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 1.770650
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 1.818515
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 5.688126
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 79.841701
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 1.253809
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 2.977911
## p-value
## (Intercept) 0.80683707
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness 0.07827142
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.14347324
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 0.58073011
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.30138350
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.32449375
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 0.58298347
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 0.01304221
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 0.13788370
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.82891284
# Patients in each category
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$awareness)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 104 | 80 |
## | 0.565 | 0.435 |
## |-----------|-----------|
##
##
##
##
# Time to first BZD
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 7.00 21.50 68.46 55.00 1264.00
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0)
## [1] 154.9925
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 184.0 184.0 21.5 20.0 30.0
# Figure time to first BZD
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 9.25 26.50 63.71 62.50 720.00
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 7.00 20.00 74.64 48.50 1264.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 104 52.8 56.6
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 80 43.7 39.9
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 0.253 0.96
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 0.360 0.96
##
## Chisq= 1 on 1 degrees of freedom, p= 0.3
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.3272417
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 184, number of events= 184
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.022029
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.426614
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.022968
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.126234
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.150251
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.137807
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.065893
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.532780
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.006216
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.090589
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.022273
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.652715
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.023234
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.134548
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.162126
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.147754
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.068113
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.703662
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.006236
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.094819
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.156733
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.169027
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.155971
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.154541
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.161143
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.188267
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.161141
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.213016
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.016130
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.154384
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.141
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -2.524
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.147
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.817
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.932
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.732
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.409
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.501
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.385
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.587
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.8882
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.0116 *
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8829
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.4140
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.3511
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.4642
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.6826
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.0124 *
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.7000
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.5574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.0223
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6527
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.0232
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.1345
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.1621
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.1478
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0681
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.7037
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0062
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.0948
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.9782
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.5321
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.9773
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8814
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.8605
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8713
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.9362
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.5870
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9938
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.9134
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.7519
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.4686
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.7537
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8381
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.8474
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.7936
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.7788
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.1222
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9749
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8090
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.3899
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.9091
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.3891
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.5359
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.5937
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.6600
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.4648
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.5864
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0386
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.4817
##
## Concordance= 0.593 (se = 0.027 )
## Rsquare= 0.088 (max possible= 1 )
## Likelihood ratio test= 16.89 on 10 df, p=0.08
## Wald test = 17.79 on 10 df, p=0.06
## Score (logrank) test = 18.34 on 10 df, p=0.05
# Time to first non-BZD AED
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 50.0 86.0 184.3 175.2 1800.0
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0)
## [1] 269.1477
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 184 184 86 69 115
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.0 45.0 81.0 188.3 182.5 1800.0
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 59.0 89.5 179.2 162.8 1276.0
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 104 53.8 51.5
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 80 39.4 41.6
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 0.0987 0.333
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 0.1221 0.333
##
## Chisq= 0.3 on 1 degrees of freedom, p= 0.6
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.5638338
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 184, number of events= 184
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness -0.08319
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.70406
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.10697
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.02363
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.07190
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology -0.05010
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy -0.08553
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.06886
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.02066
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.11682
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.92017
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.49457
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.11291
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.02392
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.93063
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.95113
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.91802
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.07128
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.97955
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.12392
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.15620
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.16931
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.15950
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.15528
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.15684
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.18474
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.16735
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.21557
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.01547
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.16076
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness -0.533
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -4.159
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.671
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.152
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.458
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology -0.271
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy -0.511
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.319
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -1.336
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.727
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.594
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 3.2e-05 ***
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.502
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.879
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.647
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.786
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.609
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.749
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.182
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.9202
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.4946
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.1129
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.0239
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.9306
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.9511
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.9180
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.0713
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9796
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.1239
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.0868
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 2.0219
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8985
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.9766
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.0745
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.0514
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0893
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.9335
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0209
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8897
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.6775
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.3549
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8141
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.7552
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.6843
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.6622
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.6613
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.7021
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9503
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8202
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.2498
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6892
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.5213
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.3882
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.2656
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.3661
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.2744
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.6345
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0097
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.5402
##
## Concordance= 0.605 (se = 0.025 )
## Rsquare= 0.118 (max possible= 1 )
## Likelihood ratio test= 23.12 on 10 df, p=0.01
## Wald test = 23.71 on 10 df, p=0.008
## Score (logrank) test = 24.56 on 10 df, p=0.006
# Time to first CI
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20.0 124.2 193.0 504.4 657.0 4320.0 100
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## 1)
##
## 100 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 84 84 193 155 330
# Figure time to first CI
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20.0 134.0 175.0 553.5 634.5 4320.0 60
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 45.0 121.5 212.0 450.4 660.5 1803.0 40
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness, rho = 1)
##
## n=84, 100 observations deleted due to missingness.
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 44 22.3 22.3
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 40 20.3 20.3
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 5.71e-05 0.00018
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 6.29e-05 0.00018
##
## Chisq= 0 on 1 degrees of freedom, p= 1
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.9893053
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 84, number of events= 84
## (100 observations deleted due to missingness)
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.174357
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.070192
## pSERG[pSERG$HOSPITALONSET == "no", ]$day -0.134197
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.698169
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.545110
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.468040
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.107528
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.181609
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.004185
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.226488
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.190481
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.072714
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.874418
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.010069
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.579778
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.596861
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.113522
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.199145
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.004194
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.254188
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.245256
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.283565
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.233913
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.263791
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.280230
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.301080
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.281619
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.321661
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.024576
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.259774
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.711
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.248
## pSERG[pSERG$HOSPITALONSET == "no", ]$day -0.574
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.647
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -1.945
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.555
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.382
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.565
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.170
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.872
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.47713
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.80449
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.56617
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.00813 **
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.05175 .
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.12006
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.70259
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.57235
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.86479
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.38328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.1905
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.0727
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8744
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.0101
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.5798
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.5969
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.1135
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.1991
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0042
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.2542
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.8400
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.9322
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.1436
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.4975
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.7248
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.6262
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.8981
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.8339
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9958
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7973
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 0.7361
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6153
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.5529
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.1986
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.3348
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8851
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.6412
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.6384
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9570
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7538
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness 1.925
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.870
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.383
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 3.371
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.004
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 2.881
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.934
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.253
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.054
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 2.087
##
## Concordance= 0.611 (se = 0.037 )
## Rsquare= 0.139 (max possible= 0.999 )
## Likelihood ratio test= 12.61 on 10 df, p=0.2
## Wald test = 12.47 on 10 df, p=0.3
## Score (logrank) test = 12.65 on 10 df, p=0.2
#### Recommendations and outliers out of the hospital
# First BZD later than 20 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 92 | 92 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 104
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 49 | 55 |
## | 0.471 | 0.529 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 80
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 43 | 37 |
## | 0.537 | 0.463 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore20min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore20min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.4572
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4098178 1.4326910
## sample estimates:
## odds ratio
## 0.7677165
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=20,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.200 -2.137 1.737 0.840
## RMST (arm=1)/(arm=0) 0.986 0.863 1.126 0.832
## RMTL (arm=1)/(arm=0) 1.033 0.710 1.502 0.866
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 18.166 1.686 10.775 0.000 14.862 21.470
## arm -0.200 0.988 -0.202 0.840 -2.137 1.737
## TYPESTATUSnumeric 0.028 1.018 0.028 0.978 -1.967 2.023
## day -0.614 0.989 -0.621 0.535 -2.551 1.324
## earlyacademicyear -0.058 0.965 -0.060 0.952 -1.951 1.834
## white -1.005 1.001 -1.004 0.316 -2.967 0.957
## structuraletiology -0.933 1.172 -0.796 0.426 -3.230 1.364
## priorepilepsy -1.890 0.991 -1.908 0.056 -3.832 0.051
## status -4.782 1.493 -3.202 0.001 -7.710 -1.855
## ageyears -0.079 0.102 -0.775 0.438 -0.279 0.121
## SEXnumeric 0.219 0.985 0.222 0.824 -1.712 2.150
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.917 0.111 26.394 0.000 18.493 14.891
## arm -0.014 0.068 -0.213 0.832 0.986 0.863
## TYPESTATUSnumeric 0.001 0.069 0.018 0.986 1.001 0.875
## day -0.043 0.067 -0.645 0.519 0.957 0.839
## earlyacademicyear -0.003 0.065 -0.045 0.964 0.997 0.877
## white -0.068 0.068 -1.000 0.317 0.935 0.819
## structuraletiology -0.063 0.080 -0.782 0.434 0.939 0.803
## priorepilepsy -0.126 0.068 -1.857 0.063 0.881 0.772
## status -0.374 0.134 -2.787 0.005 0.688 0.529
## ageyears -0.006 0.007 -0.796 0.426 0.994 0.981
## SEXnumeric 0.017 0.068 0.246 0.806 1.017 0.891
## upper .95
## intercept 22.966
## arm 1.126
## TYPESTATUSnumeric 1.146
## day 1.093
## earlyacademicyear 1.133
## white 1.067
## structuraletiology 1.099
## priorepilepsy 1.007
## status 0.895
## ageyears 1.008
## SEXnumeric 1.161
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 0.946 0.378 2.507 0.012 2.577 1.229
## arm 0.032 0.191 0.168 0.866 1.033 0.710
## TYPESTATUSnumeric -0.007 0.200 -0.036 0.972 0.993 0.671
## day 0.106 0.190 0.560 0.575 1.112 0.767
## earlyacademicyear 0.018 0.191 0.096 0.923 1.019 0.700
## white 0.195 0.200 0.975 0.330 1.216 0.821
## structuraletiology 0.181 0.223 0.814 0.416 1.199 0.775
## priorepilepsy 0.393 0.205 1.919 0.055 1.482 0.992
## status 0.709 0.200 3.554 0.000 2.032 1.374
## ageyears 0.013 0.019 0.686 0.493 1.013 0.976
## SEXnumeric -0.028 0.189 -0.150 0.881 0.972 0.671
## upper .95
## intercept 5.400
## arm 1.502
## TYPESTATUSnumeric 1.470
## day 1.614
## earlyacademicyear 1.482
## white 1.800
## structuraletiology 1.855
## priorepilepsy 2.215
## status 3.005
## ageyears 1.052
## SEXnumeric 1.408
# First BZD later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 123 | 61 |
## | 0.668 | 0.332 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 104
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 67 | 37 |
## | 0.644 | 0.356 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 80
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 56 | 24 |
## | 0.700 | 0.300 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore40min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore40min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.4354
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3944499 1.5136049
## sample estimates:
## odds ratio
## 0.7771317
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.169 -5.442 3.104 0.592
## RMST (arm=1)/(arm=0) 0.949 0.786 1.147 0.591
## RMTL (arm=1)/(arm=0) 1.071 0.834 1.375 0.592
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 28.607 3.467 8.250 0.000 21.811 35.403
## arm -1.169 2.180 -0.536 0.592 -5.442 3.104
## TYPESTATUSnumeric -2.849 2.163 -1.318 0.188 -7.088 1.389
## day -0.639 2.168 -0.295 0.768 -4.889 3.610
## earlyacademicyear -0.677 2.085 -0.325 0.745 -4.765 3.410
## white -0.465 2.187 -0.213 0.832 -4.751 3.820
## structuraletiology 0.202 2.601 0.077 0.938 -4.895 5.298
## priorepilepsy -0.914 2.184 -0.418 0.676 -5.195 3.367
## status -8.573 3.058 -2.803 0.005 -14.566 -2.579
## ageyears -0.178 0.215 -0.827 0.408 -0.600 0.244
## SEXnumeric -0.112 2.168 -0.052 0.959 -4.361 4.136
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.371 0.145 23.286 0.000 29.100 21.912
## arm -0.052 0.096 -0.538 0.591 0.949 0.786
## TYPESTATUSnumeric -0.125 0.096 -1.302 0.193 0.882 0.730
## day -0.032 0.094 -0.336 0.737 0.969 0.805
## earlyacademicyear -0.026 0.090 -0.288 0.774 0.974 0.817
## white -0.017 0.095 -0.176 0.860 0.983 0.816
## structuraletiology 0.011 0.109 0.099 0.921 1.011 0.817
## priorepilepsy -0.042 0.093 -0.455 0.649 0.958 0.798
## status -0.442 0.182 -2.421 0.015 0.643 0.450
## ageyears -0.008 0.010 -0.819 0.413 0.992 0.974
## SEXnumeric 0.000 0.094 0.000 1.000 1.000 0.832
## upper .95
## intercept 38.646
## arm 1.147
## TYPESTATUSnumeric 1.065
## day 1.165
## earlyacademicyear 1.162
## white 1.186
## structuraletiology 1.251
## priorepilepsy 1.151
## status 0.919
## ageyears 1.011
## SEXnumeric 1.202
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.485 0.221 11.224 0.000 12.000 7.776
## arm 0.068 0.128 0.536 0.592 1.071 0.834
## TYPESTATUSnumeric 0.168 0.127 1.321 0.187 1.182 0.922
## day 0.031 0.129 0.240 0.810 1.032 0.800
## earlyacademicyear 0.046 0.125 0.370 0.711 1.047 0.820
## white 0.034 0.129 0.260 0.795 1.034 0.803
## structuraletiology -0.009 0.163 -0.056 0.956 0.991 0.720
## priorepilepsy 0.050 0.134 0.372 0.710 1.051 0.808
## status 0.433 0.146 2.970 0.003 1.542 1.159
## ageyears 0.010 0.013 0.830 0.407 1.010 0.986
## SEXnumeric 0.015 0.130 0.116 0.908 1.015 0.787
## upper .95
## intercept 18.519
## arm 1.375
## TYPESTATUSnumeric 1.516
## day 1.329
## earlyacademicyear 1.338
## white 1.332
## structuraletiology 1.364
## priorepilepsy 1.367
## status 2.052
## ageyears 1.036
## SEXnumeric 1.309
# First BZD later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 144 | 40 |
## | 0.783 | 0.217 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 104
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 78 | 26 |
## | 0.750 | 0.250 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 80
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 66 | 14 |
## | 0.825 | 0.175 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.2799
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2831795 1.3884282
## sample estimates:
## odds ratio
## 0.6379075
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.000 -8.311 4.312 0.535
## RMST (arm=1)/(arm=0) 0.932 0.743 1.169 0.543
## RMTL (arm=1)/(arm=0) 1.066 0.874 1.301 0.526
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 37.020 5.138 7.205 0.000 26.950 47.091
## arm -2.000 3.220 -0.621 0.535 -8.311 4.312
## TYPESTATUSnumeric -5.881 3.163 -1.859 0.063 -12.080 0.319
## day -0.980 3.213 -0.305 0.760 -7.277 5.316
## earlyacademicyear -1.408 3.092 -0.455 0.649 -7.467 4.651
## white -0.941 3.250 -0.290 0.772 -7.312 5.429
## structuraletiology 0.746 3.846 0.194 0.846 -6.793 8.284
## priorepilepsy 1.503 3.264 0.460 0.645 -4.895 7.900
## status -12.914 4.083 -3.163 0.002 -20.916 -4.911
## ageyears -0.292 0.311 -0.940 0.347 -0.902 0.317
## SEXnumeric -0.966 3.216 -0.301 0.764 -7.269 5.336
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.633 0.171 21.206 0.000 37.814 27.030
## arm -0.070 0.116 -0.608 0.543 0.932 0.743
## TYPESTATUSnumeric -0.209 0.116 -1.806 0.071 0.811 0.646
## day -0.038 0.112 -0.338 0.735 0.963 0.773
## earlyacademicyear -0.043 0.107 -0.402 0.688 0.958 0.776
## white -0.027 0.114 -0.240 0.810 0.973 0.777
## structuraletiology 0.027 0.127 0.213 0.831 1.028 0.801
## priorepilepsy 0.045 0.111 0.411 0.681 1.046 0.843
## status -0.548 0.206 -2.666 0.008 0.578 0.386
## ageyears -0.010 0.011 -0.923 0.356 0.990 0.969
## SEXnumeric -0.026 0.112 -0.236 0.813 0.974 0.783
## upper .95
## intercept 52.901
## arm 1.169
## TYPESTATUSnumeric 1.018
## day 1.199
## earlyacademicyear 1.182
## white 1.218
## structuraletiology 1.319
## priorepilepsy 1.300
## status 0.865
## ageyears 1.012
## SEXnumeric 1.212
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.164 0.175 18.055 0.000 23.659 16.782
## arm 0.064 0.102 0.633 0.526 1.066 0.874
## TYPESTATUSnumeric 0.187 0.100 1.864 0.062 1.205 0.990
## day 0.029 0.104 0.276 0.783 1.029 0.839
## earlyacademicyear 0.050 0.100 0.502 0.615 1.051 0.865
## white 0.034 0.104 0.331 0.740 1.035 0.844
## structuraletiology -0.024 0.130 -0.181 0.856 0.977 0.756
## priorepilepsy -0.054 0.108 -0.497 0.619 0.948 0.767
## status 0.368 0.112 3.272 0.001 1.445 1.159
## ageyears 0.009 0.010 0.945 0.345 1.009 0.990
## SEXnumeric 0.037 0.105 0.351 0.726 1.037 0.845
## upper .95
## intercept 33.353
## arm 1.301
## TYPESTATUSnumeric 1.467
## day 1.261
## earlyacademicyear 1.278
## white 1.269
## structuraletiology 1.261
## priorepilepsy 1.171
## status 1.801
## ageyears 1.029
## SEXnumeric 1.273
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 33 | 151 |
## | 0.179 | 0.821 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 104
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 23 | 81 |
## | 0.221 | 0.779 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 80
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 10 | 70 |
## | 0.125 | 0.875 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore40min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore40min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.1208
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.8375416 4.9984480
## sample estimates:
## odds ratio
## 1.980487
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.030 -2.201 2.141 0.978
## RMST (arm=1)/(arm=0) 0.999 0.943 1.059 0.985
## RMTL (arm=1)/(arm=0) 1.056 0.468 2.386 0.895
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.368 2.061 16.671 0.000 30.327 38.408
## arm -0.030 1.108 -0.027 0.978 -2.201 2.141
## TYPESTATUSnumeric -0.866 1.023 -0.846 0.397 -2.872 1.140
## day -0.397 1.140 -0.348 0.728 -2.631 1.837
## earlyacademicyear 1.519 1.057 1.438 0.151 -0.552 3.591
## white 0.819 1.101 0.744 0.457 -1.339 2.976
## structuraletiology 0.740 1.226 0.603 0.546 -1.664 3.143
## priorepilepsy 1.625 1.159 1.403 0.161 -0.646 3.896
## status 0.889 1.195 0.744 0.457 -1.453 3.231
## ageyears 0.069 0.112 0.617 0.537 -0.151 0.289
## SEXnumeric 1.344 1.104 1.217 0.224 -0.820 3.508
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.539 0.057 62.268 0.000 34.437 30.806
## arm -0.001 0.030 -0.019 0.985 0.999 0.943
## TYPESTATUSnumeric -0.023 0.028 -0.838 0.402 0.977 0.926
## day -0.011 0.031 -0.350 0.726 0.989 0.932
## earlyacademicyear 0.041 0.029 1.422 0.155 1.041 0.985
## white 0.022 0.030 0.747 0.455 1.022 0.965
## structuraletiology 0.020 0.033 0.607 0.544 1.020 0.957
## priorepilepsy 0.044 0.031 1.395 0.163 1.045 0.982
## status 0.023 0.032 0.739 0.460 1.024 0.962
## ageyears 0.002 0.003 0.629 0.529 1.002 0.996
## SEXnumeric 0.036 0.030 1.205 0.228 1.037 0.978
## upper .95
## intercept 38.495
## arm 1.059
## TYPESTATUSnumeric 1.031
## day 1.050
## earlyacademicyear 1.101
## white 1.084
## structuraletiology 1.088
## priorepilepsy 1.111
## status 1.089
## ageyears 1.008
## SEXnumeric 1.099
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.884 0.582 3.236 0.001 6.581 2.102
## arm 0.055 0.416 0.132 0.895 1.056 0.468
## TYPESTATUSnumeric 0.347 0.381 0.911 0.362 1.415 0.671
## day 0.144 0.441 0.326 0.744 1.155 0.487
## earlyacademicyear -0.632 0.420 -1.506 0.132 0.531 0.233
## white -0.254 0.392 -0.648 0.517 0.776 0.359
## structuraletiology -0.274 0.479 -0.573 0.567 0.760 0.297
## priorepilepsy -0.640 0.467 -1.371 0.170 0.527 0.211
## status -0.448 0.605 -0.741 0.459 0.639 0.195
## ageyears -0.021 0.048 -0.434 0.664 0.979 0.891
## SEXnumeric -0.526 0.408 -1.287 0.198 0.591 0.266
## upper .95
## intercept 20.600
## arm 2.386
## TYPESTATUSnumeric 2.987
## day 2.740
## earlyacademicyear 1.210
## white 1.673
## structuraletiology 1.944
## priorepilepsy 1.316
## status 2.092
## ageyears 1.077
## SEXnumeric 1.316
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 64 | 120 |
## | 0.348 | 0.652 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 104
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 40 | 64 |
## | 0.385 | 0.615 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 80
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 24 | 56 |
## | 0.300 | 0.700 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.2752
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.751433 2.855896
## sample estimates:
## odds ratio
## 1.455339
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 2.272 -1.688 6.232 0.261
## RMST (arm=1)/(arm=0) 1.045 0.969 1.127 0.253
## RMTL (arm=1)/(arm=0) 0.754 0.428 1.329 0.329
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 45.873 3.766 12.180 0.000 38.491 53.255
## arm 2.272 2.021 1.125 0.261 -1.688 6.232
## TYPESTATUSnumeric -2.625 1.988 -1.320 0.187 -6.522 1.272
## day -1.368 2.096 -0.653 0.514 -5.475 2.740
## earlyacademicyear 3.026 2.008 1.507 0.132 -0.910 6.962
## white 1.090 2.093 0.521 0.602 -3.012 5.191
## structuraletiology 0.818 2.430 0.336 0.737 -3.946 5.581
## priorepilepsy 4.116 2.139 1.925 0.054 -0.076 8.307
## status 1.694 2.253 0.752 0.452 -2.721 6.109
## ageyears 0.177 0.208 0.848 0.396 -0.232 0.585
## SEXnumeric 2.804 2.063 1.359 0.174 -1.240 6.848
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.830 0.075 50.957 0.000 46.077 39.765
## arm 0.044 0.038 1.142 0.253 1.045 0.969
## TYPESTATUSnumeric -0.050 0.039 -1.290 0.197 0.951 0.882
## day -0.027 0.040 -0.665 0.506 0.974 0.900
## earlyacademicyear 0.058 0.039 1.495 0.135 1.059 0.982
## white 0.022 0.041 0.534 0.593 1.022 0.944
## structuraletiology 0.016 0.047 0.345 0.730 1.016 0.927
## priorepilepsy 0.079 0.041 1.916 0.055 1.082 0.998
## status 0.031 0.042 0.745 0.456 1.032 0.950
## ageyears 0.003 0.004 0.873 0.383 1.003 0.996
## SEXnumeric 0.054 0.040 1.339 0.181 1.055 0.975
## upper .95
## intercept 53.391
## arm 1.127
## TYPESTATUSnumeric 1.026
## day 1.053
## earlyacademicyear 1.143
## white 1.106
## structuraletiology 1.114
## priorepilepsy 1.174
## status 1.120
## ageyears 1.011
## SEXnumeric 1.141
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.739 0.407 6.737 0.000 15.475 6.975
## arm -0.282 0.289 -0.977 0.329 0.754 0.428
## TYPESTATUSnumeric 0.358 0.249 1.437 0.151 1.431 0.878
## day 0.163 0.287 0.569 0.570 1.177 0.671
## earlyacademicyear -0.420 0.281 -1.493 0.135 0.657 0.379
## white -0.112 0.260 -0.430 0.667 0.894 0.537
## structuraletiology -0.091 0.309 -0.293 0.770 0.913 0.498
## priorepilepsy -0.545 0.304 -1.791 0.073 0.580 0.320
## status -0.268 0.375 -0.715 0.475 0.765 0.367
## ageyears -0.021 0.032 -0.656 0.512 0.979 0.920
## SEXnumeric -0.382 0.266 -1.437 0.151 0.683 0.406
## upper .95
## intercept 34.332
## arm 1.329
## TYPESTATUSnumeric 2.331
## day 2.067
## earlyacademicyear 1.140
## white 1.489
## structuraletiology 1.674
## priorepilepsy 1.053
## status 1.595
## ageyears 1.043
## SEXnumeric 1.149
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 115 | 69 |
## | 0.625 | 0.375 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 104
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 66 | 38 |
## | 0.635 | 0.365 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 80
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 49 | 31 |
## | 0.613 | 0.388 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore120min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore120min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.7614
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.575250 2.092045
## sample estimates:
## odds ratio
## 1.098254
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, tau=120,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 4.405 -6.157 14.967 0.414
## RMST (arm=1)/(arm=0) 1.055 0.928 1.199 0.414
## RMTL (arm=1)/(arm=0) 0.889 0.668 1.182 0.417
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 78.960 8.712 9.063 0.000 61.884 96.036
## arm 4.405 5.389 0.817 0.414 -6.157 14.967
## TYPESTATUSnumeric -21.119 5.338 -3.956 0.000 -31.581 -10.656
## day -3.537 5.352 -0.661 0.509 -14.027 6.954
## earlyacademicyear 3.312 5.308 0.624 0.533 -7.090 13.715
## white 1.138 5.538 0.205 0.837 -9.717 11.992
## structuraletiology -2.087 6.705 -0.311 0.756 -15.229 11.055
## priorepilepsy 9.024 5.564 1.622 0.105 -1.882 19.931
## status -1.581 6.676 -0.237 0.813 -14.666 11.504
## ageyears 0.514 0.533 0.963 0.335 -0.532 1.559
## SEXnumeric 4.154 5.427 0.765 0.444 -6.484 14.792
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.361 0.109 39.871 0.000 78.305 63.197
## arm 0.053 0.065 0.818 0.414 1.055 0.928
## TYPESTATUSnumeric -0.267 0.071 -3.778 0.000 0.765 0.666
## day -0.043 0.065 -0.656 0.512 0.958 0.843
## earlyacademicyear 0.042 0.065 0.647 0.518 1.043 0.919
## white 0.014 0.069 0.199 0.842 1.014 0.886
## structuraletiology -0.024 0.083 -0.286 0.775 0.977 0.830
## priorepilepsy 0.109 0.068 1.607 0.108 1.115 0.976
## status -0.019 0.082 -0.226 0.821 0.982 0.836
## ageyears 0.006 0.006 1.011 0.312 1.006 0.994
## SEXnumeric 0.051 0.067 0.760 0.447 1.052 0.923
## upper .95
## intercept 97.025
## arm 1.199
## TYPESTATUSnumeric 0.879
## day 1.089
## earlyacademicyear 1.183
## white 1.160
## structuraletiology 1.149
## priorepilepsy 1.274
## status 1.152
## ageyears 1.018
## SEXnumeric 1.199
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.672 0.227 16.185 0.000 39.338 25.217
## arm -0.118 0.145 -0.812 0.417 0.889 0.668
## TYPESTATUSnumeric 0.535 0.139 3.841 0.000 1.707 1.299
## day 0.096 0.145 0.657 0.511 1.100 0.827
## earlyacademicyear -0.081 0.146 -0.556 0.579 0.922 0.693
## white -0.031 0.143 -0.219 0.827 0.969 0.732
## structuraletiology 0.063 0.172 0.365 0.715 1.065 0.759
## priorepilepsy -0.245 0.153 -1.606 0.108 0.782 0.580
## status 0.050 0.177 0.281 0.778 1.051 0.742
## ageyears -0.014 0.017 -0.847 0.397 0.986 0.954
## SEXnumeric -0.110 0.145 -0.760 0.447 0.896 0.675
## upper .95
## intercept 61.367
## arm 1.182
## TYPESTATUSnumeric 2.242
## day 1.463
## earlyacademicyear 1.227
## white 1.282
## structuraletiology 1.493
## priorepilepsy 1.056
## status 1.488
## ageyears 1.019
## SEXnumeric 1.189
# First CI later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 78 |
## | 0.071 | 0.929 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 44
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 41 |
## | 0.068 | 0.932 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 37 |
## | 0.075 | 0.925 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1138188 7.1711526
## sample estimates:
## odds ratio
## 0.9035448
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.868 -1.037 2.773 0.372
## RMST (arm=1)/(arm=0) 1.015 0.982 1.049 0.378
## RMTL (arm=1)/(arm=0) 0.349 0.039 3.116 0.346
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 59.711 1.067 55.977 0.000 57.620 61.801
## arm 0.868 0.972 0.893 0.372 -1.037 2.773
## TYPESTATUSnumeric 0.345 0.621 0.555 0.579 -0.872 1.562
## day -0.143 1.036 -0.138 0.890 -2.173 1.888
## earlyacademicyear -1.947 1.111 -1.753 0.080 -4.125 0.230
## white -1.000 1.070 -0.934 0.350 -3.096 1.097
## structuraletiology 0.554 0.558 0.994 0.320 -0.539 1.648
## priorepilepsy -0.950 1.170 -0.812 0.417 -3.242 1.342
## status 1.426 0.924 1.544 0.123 -0.384 3.236
## ageyears -0.061 0.066 -0.923 0.356 -0.190 0.068
## SEXnumeric 1.619 1.399 1.157 0.247 -1.123 4.361
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.089 0.018 222.750 0.000 59.686 57.577
## arm 0.015 0.017 0.882 0.378 1.015 0.982
## TYPESTATUSnumeric 0.006 0.010 0.544 0.586 1.006 0.985
## day -0.002 0.018 -0.125 0.900 0.998 0.964
## earlyacademicyear -0.033 0.019 -1.725 0.084 0.968 0.932
## white -0.017 0.018 -0.924 0.356 0.983 0.948
## structuraletiology 0.009 0.010 0.979 0.327 1.009 0.991
## priorepilepsy -0.016 0.020 -0.804 0.422 0.984 0.946
## status 0.024 0.016 1.529 0.126 1.024 0.993
## ageyears -0.001 0.001 -0.916 0.360 0.999 0.997
## SEXnumeric 0.028 0.024 1.139 0.255 1.028 0.980
## upper .95
## intercept 61.873
## arm 1.049
## TYPESTATUSnumeric 1.027
## day 1.033
## earlyacademicyear 1.004
## white 1.019
## structuraletiology 1.029
## priorepilepsy 1.023
## status 1.056
## ageyears 1.001
## SEXnumeric 1.078
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept -4.309 3.369 -1.279 0.201 0.013 0.000
## arm -1.052 1.117 -0.942 0.346 0.349 0.039
## TYPESTATUSnumeric -1.227 1.663 -0.738 0.461 0.293 0.011
## day 1.768 1.968 0.899 0.369 5.860 0.124
## earlyacademicyear 3.244 1.241 2.613 0.009 25.631 2.249
## white 1.487 1.799 0.827 0.408 4.426 0.130
## structuraletiology -1.817 1.414 -1.285 0.199 0.163 0.010
## priorepilepsy 1.820 1.258 1.447 0.148 6.172 0.524
## status -18.014 1.171 -15.389 0.000 0.000 0.000
## ageyears 0.020 0.068 0.292 0.770 1.020 0.892
## SEXnumeric -1.859 1.232 -1.509 0.131 0.156 0.014
## upper .95
## intercept 9.905
## arm 3.116
## TYPESTATUSnumeric 7.630
## day 277.142
## earlyacademicyear 292.038
## white 150.436
## structuraletiology 2.596
## priorepilepsy 72.654
## status 0.000
## ageyears 1.166
## SEXnumeric 1.743
# First CI later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 64 |
## | 0.238 | 0.762 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 44
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 10 | 34 |
## | 0.227 | 0.773 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 10 | 30 |
## | 0.250 | 0.750 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore120min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore120min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2855764 2.7315767
## sample estimates:
## odds ratio
## 0.8836722
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.539 -10.107 9.028 0.912
## RMST (arm=1)/(arm=0) 0.994 0.910 1.086 0.899
## RMTL (arm=1)/(arm=0) 0.948 0.378 2.375 0.909
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 111.102 8.640 12.859 0.000 94.167 128.037
## arm -0.539 4.882 -0.111 0.912 -10.107 9.028
## TYPESTATUSnumeric 2.442 4.783 0.510 0.610 -6.933 11.817
## day -4.092 5.048 -0.811 0.418 -13.986 5.802
## earlyacademicyear -8.922 5.315 -1.678 0.093 -19.339 1.496
## white 6.470 5.312 1.218 0.223 -3.942 16.881
## structuraletiology 1.333 5.935 0.225 0.822 -10.300 12.966
## priorepilepsy -5.198 5.520 -0.942 0.346 -16.017 5.621
## status 9.993 4.750 2.104 0.035 0.683 19.304
## ageyears 0.080 0.436 0.182 0.855 -0.775 0.935
## SEXnumeric 1.098 5.093 0.216 0.829 -8.884 11.080
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.709 0.079 59.673 0.000 110.989 95.083
## arm -0.006 0.045 -0.128 0.899 0.994 0.910
## TYPESTATUSnumeric 0.023 0.044 0.532 0.595 1.023 0.940
## day -0.038 0.046 -0.819 0.413 0.963 0.880
## earlyacademicyear -0.082 0.049 -1.658 0.097 0.922 0.837
## white 0.059 0.049 1.217 0.223 1.061 0.964
## structuraletiology 0.011 0.055 0.200 0.841 1.011 0.908
## priorepilepsy -0.047 0.051 -0.926 0.354 0.954 0.863
## status 0.089 0.043 2.083 0.037 1.093 1.005
## ageyears 0.001 0.004 0.177 0.860 1.001 0.993
## SEXnumeric 0.010 0.046 0.220 0.825 1.010 0.922
## upper .95
## intercept 129.556
## arm 1.086
## TYPESTATUSnumeric 1.115
## day 1.054
## earlyacademicyear 1.015
## white 1.168
## structuraletiology 1.126
## priorepilepsy 1.054
## status 1.189
## ageyears 1.009
## SEXnumeric 1.107
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.005 0.984 2.037 0.042 7.424 1.079
## arm -0.054 0.469 -0.115 0.909 0.948 0.378
## TYPESTATUSnumeric -0.148 0.520 -0.285 0.775 0.862 0.311
## day 0.374 0.529 0.707 0.480 1.453 0.515
## earlyacademicyear 0.944 0.619 1.524 0.127 2.571 0.763
## white -0.613 0.553 -1.109 0.267 0.541 0.183
## structuraletiology -0.216 0.600 -0.360 0.719 0.806 0.248
## priorepilepsy 0.540 0.518 1.043 0.297 1.716 0.622
## status -1.411 0.786 -1.796 0.073 0.244 0.052
## ageyears -0.009 0.045 -0.210 0.834 0.991 0.907
## SEXnumeric -0.078 0.524 -0.148 0.882 0.925 0.332
## upper .95
## intercept 51.081
## arm 2.375
## TYPESTATUSnumeric 2.387
## day 4.097
## earlyacademicyear 8.658
## white 1.601
## structuraletiology 2.612
## priorepilepsy 4.738
## status 1.138
## ageyears 1.082
## SEXnumeric 2.582
# First CI later than 240 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 48 | 36 |
## | 0.571 | 0.429 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 44
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 26 | 18 |
## | 0.591 | 0.409 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness == 1, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 22 | 18 |
## | 0.550 | 0.450 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore240min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore240min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness
## p-value = 0.8258
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4553598 3.0670885
## sample estimates:
## odds ratio
## 1.179461
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 7.108 -21.777 35.993 0.630
## RMST (arm=1)/(arm=0) 1.038 0.880 1.225 0.655
## RMTL (arm=1)/(arm=0) 0.879 0.560 1.379 0.575
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 186.160 26.456 7.037 0.000 134.306 238.013
## arm 7.108 14.738 0.482 0.630 -21.777 35.993
## TYPESTATUSnumeric -18.507 16.350 -1.132 0.258 -50.553 13.539
## day -7.458 15.821 -0.471 0.637 -38.467 23.551
## earlyacademicyear -28.063 15.408 -1.821 0.069 -58.262 2.136
## white 22.826 16.055 1.422 0.155 -8.641 54.292
## structuraletiology -5.083 18.279 -0.278 0.781 -40.910 30.743
## priorepilepsy 7.979 16.867 0.473 0.636 -25.079 41.037
## status 11.422 16.611 0.688 0.492 -21.135 43.978
## ageyears -0.619 1.408 -0.440 0.660 -3.378 2.140
## SEXnumeric -8.264 14.425 -0.573 0.567 -36.536 20.009
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.218 0.148 35.216 0.000 184.504 138.004
## arm 0.038 0.084 0.446 0.655 1.038 0.880
## TYPESTATUSnumeric -0.108 0.097 -1.113 0.266 0.898 0.742
## day -0.038 0.088 -0.431 0.667 0.963 0.809
## earlyacademicyear -0.158 0.088 -1.802 0.072 0.854 0.719
## white 0.132 0.095 1.386 0.166 1.141 0.947
## structuraletiology -0.029 0.106 -0.275 0.784 0.971 0.790
## priorepilepsy 0.046 0.094 0.492 0.623 1.047 0.872
## status 0.069 0.092 0.750 0.454 1.072 0.894
## ageyears -0.004 0.008 -0.447 0.655 0.996 0.981
## SEXnumeric -0.046 0.082 -0.564 0.573 0.955 0.813
## upper .95
## intercept 246.673
## arm 1.225
## TYPESTATUSnumeric 1.086
## day 1.145
## earlyacademicyear 1.014
## white 1.374
## structuraletiology 1.195
## priorepilepsy 1.258
## status 1.284
## ageyears 1.012
## SEXnumeric 1.121
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.908 0.459 8.512 0.000 49.821 20.256
## arm -0.129 0.230 -0.561 0.575 0.879 0.560
## TYPESTATUSnumeric 0.275 0.240 1.144 0.253 1.316 0.822
## day 0.154 0.268 0.574 0.566 1.166 0.689
## earlyacademicyear 0.465 0.266 1.749 0.080 1.593 0.945
## white -0.347 0.242 -1.436 0.151 0.707 0.440
## structuraletiology 0.084 0.279 0.299 0.765 1.087 0.629
## priorepilepsy -0.116 0.287 -0.405 0.686 0.890 0.508
## status -0.152 0.288 -0.529 0.597 0.859 0.489
## ageyears 0.009 0.023 0.410 0.682 1.010 0.965
## SEXnumeric 0.137 0.234 0.584 0.559 1.147 0.724
## upper .95
## intercept 122.540
## arm 1.379
## TYPESTATUSnumeric 2.107
## day 1.973
## earlyacademicyear 2.683
## white 1.135
## structuraletiology 1.880
## priorepilepsy 1.562
## status 1.510
## ageyears 1.056
## SEXnumeric 1.816
## IN THE HOSPITAL
# Patients in each group
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 46 | 38 |
## | 0.548 | 0.452 |
## |-----------|-----------|
##
##
##
##
# Time to first BZD
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 4.00 10.00 30.83 24.25 360.00
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0)
## [1] 62.38161
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 84 84 10 5 16
# Figure time to first BZD
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 4.25 8.00 37.35 25.75 360.00
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 4.00 11.00 22.95 23.75 205.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 46 24.4 24.2
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 38 19.6 19.8
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 0.00150 0.00518
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 0.00183 0.00518
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.9426513
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 84, number of events= 84
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.20359
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.09299
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.27563
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.44339
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.01725
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.03145
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.04092
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.04204
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.02085
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.07752
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.22579
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.91120
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.31736
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.55798
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.98290
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.96904
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.04176
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.04294
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.97937
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.08060
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.24045
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.30879
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.27596
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.25452
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.24402
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.26061
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.30875
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.34202
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.02320
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.25721
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.847
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.301
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.999
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.742
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.071
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.121
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.133
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.123
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.899
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.301
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.3972
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.7633
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.3179
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.0815 .
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.9436
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.9039
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.8946
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.9022
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.3689
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.7631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.2258
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.9112
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.3174
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.5580
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.9829
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.9690
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.0418
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.0429
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9794
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.0806
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.8158
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.0975
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.7591
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.6419
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.0174
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.0320
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.9599
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.9588
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0211
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.9254
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.7652
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.4975
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.7670
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.9460
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.6093
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.5814
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.5688
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.5335
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9358
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.6527
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.964
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.669
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.263
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 2.566
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.586
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.615
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.908
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.039
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.025
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.789
##
## Concordance= 0.599 (se = 0.04 )
## Rsquare= 0.096 (max possible= 0.999 )
## Likelihood ratio test= 8.44 on 10 df, p=0.6
## Wald test = 8.44 on 10 df, p=0.6
## Score (logrank) test = 8.6 on 10 df, p=0.6
# Time to first non-BZD AED
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 21.75 39.00 85.01 76.25 1419.00
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0)
## [1] 171.8644
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 84 84 39 29 58
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 20.25 44.50 79.70 86.75 503.00
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 23.00 32.00 91.45 73.25 1419.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 46 22.9 24.2
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 38 20.1 18.8
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 0.0709 0.244
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 0.0912 0.244
##
## Chisq= 0.2 on 1 degrees of freedom, p= 0.6
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.6209923
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 84, number of events= 84
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.17987
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.25701
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.47509
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.24427
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.09484
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.47641
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.29502
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.20221
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.04027
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.11923
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.19706
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.77336
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.60817
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.27668
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.90952
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.61028
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.34315
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.22411
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.96053
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.88760
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.23451
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.29931
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.26435
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.24968
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.25907
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.26620
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.29510
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.34282
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.02291
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.26059
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.767
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.859
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.797
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.978
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.366
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.790
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.000
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.590
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -1.758
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.458
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.4431
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.3905
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.0723 .
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.3279
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.7143
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.0735 .
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.3175
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.5553
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.0788 .
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.6473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.1971
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.7734
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.6082
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.2767
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.9095
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.6103
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.3431
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.2241
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9605
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.8876
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.8354
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.2931
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.6218
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.7833
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.0995
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.6210
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.7445
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.8169
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0411
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.1266
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.7560
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.4301
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.9579
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.7826
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.5474
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.9557
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.7532
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.6252
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9184
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.5326
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.896
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.390
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.700
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 2.083
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.511
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 2.713
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 2.395
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.397
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.005
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.479
##
## Concordance= 0.603 (se = 0.038 )
## Rsquare= 0.165 (max possible= 0.999 )
## Likelihood ratio test= 15.19 on 10 df, p=0.1
## Wald test = 15.06 on 10 df, p=0.1
## Score (logrank) test = 15.39 on 10 df, p=0.1
# Time to first CI
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 122.0 210.0 569.9 495.0 7200.0 49
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## 1)
##
## 49 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 35 35 210 165 420
# Figure time to first CI
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 121.0 210.0 386.6 462.0 2520.0 23
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 112.0 131.8 205.0 921.2 535.5 7200.0 26
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness, rho = 1)
##
## n=35, 49 observations deleted due to missingness.
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 23 12.43 11.46
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 12 5.74 6.71
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 0.0824 0.337
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 0.1405 0.337
##
## Chisq= 0.3 on 1 degrees of freedom, p= 0.6
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.5615184
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2014", "2015-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 35, number of events= 35
## (49 observations deleted due to missingness)
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness -0.13006
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.48181
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.12130
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.49313
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.13659
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.68336
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.87712
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status -0.35798
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.04150
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.91148
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.87804
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.61766
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.12896
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.63743
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.14636
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.50492
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 2.40397
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.69908
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.95935
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 2.48801
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.42653
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.53007
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.43882
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.43057
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.45245
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.49232
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.60658
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.78838
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.04388
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.47372
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness -0.305
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.909
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.276
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.145
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.302
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -1.388
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.446
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status -0.454
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.946
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.924
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.7604
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.3634
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.7822
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.2521
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.7627
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.1651
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.1482
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.6498
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.3443
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.0543 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.8780
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.6177
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.1290
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.6374
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.1464
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.5049
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 2.4040
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.6991
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9594
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 2.4880
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 1.1389
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.6190
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.8858
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.6107
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.8723
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.9805
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.4160
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.4304
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0424
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.4019
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 0.3806
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.2186
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.4777
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.7041
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.4723
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.1924
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.7322
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.1491
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.8803
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.9832
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness 2.026
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.746
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.668
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 3.808
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 2.783
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.325
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 7.893
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 3.278
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.046
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 6.296
##
## Concordance= 0.645 (se = 0.06 )
## Rsquare= 0.284 (max possible= 0.995 )
## Likelihood ratio test= 11.68 on 10 df, p=0.3
## Wald test = 10.51 on 10 df, p=0.4
## Score (logrank) test = 11.63 on 10 df, p=0.3
#### Recommendations and outliers in the hospital
# First BZD later than 20 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 57 | 27 |
## | 0.679 | 0.321 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 32 | 14 |
## | 0.696 | 0.304 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 38
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 25 | 13 |
## | 0.658 | 0.342 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore20min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore20min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.8155
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4275735 3.2848535
## sample estimates:
## odds ratio
## 1.186115
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=20,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.215 -2.895 3.325 0.892
## RMST (arm=1)/(arm=0) 1.030 0.777 1.366 0.836
## RMTL (arm=1)/(arm=0) 0.990 0.692 1.418 0.958
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 10.006 2.362 4.237 0.000 5.377 14.635
## arm 0.215 1.587 0.136 0.892 -2.895 3.325
## TYPESTATUSnumeric -0.098 1.850 -0.053 0.958 -3.724 3.529
## day -2.159 1.600 -1.350 0.177 -5.294 0.976
## earlyacademicyear -2.253 1.686 -1.336 0.181 -5.557 1.051
## white 0.983 1.708 0.575 0.565 -2.366 4.331
## structuraletiology -0.263 1.747 -0.150 0.880 -3.686 3.161
## priorepilepsy 1.780 1.877 0.948 0.343 -1.899 5.460
## status 0.317 2.037 0.156 0.876 -3.676 4.310
## ageyears 0.249 0.159 1.566 0.117 -0.063 0.561
## SEXnumeric 0.845 1.779 0.475 0.635 -2.641 4.331
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.293 0.222 10.319 0.000 9.901 6.406
## arm 0.030 0.144 0.207 0.836 1.030 0.777
## TYPESTATUSnumeric -0.018 0.172 -0.105 0.916 0.982 0.700
## day -0.182 0.144 -1.261 0.207 0.834 0.629
## earlyacademicyear -0.202 0.157 -1.285 0.199 0.817 0.600
## white 0.079 0.162 0.486 0.627 1.082 0.787
## structuraletiology -0.018 0.161 -0.113 0.910 0.982 0.716
## priorepilepsy 0.151 0.167 0.904 0.366 1.163 0.838
## status 0.043 0.166 0.261 0.794 1.044 0.754
## ageyears 0.021 0.014 1.545 0.122 1.021 0.994
## SEXnumeric 0.062 0.165 0.377 0.706 1.064 0.770
## upper .95
## intercept 15.304
## arm 1.366
## TYPESTATUSnumeric 1.377
## day 1.106
## earlyacademicyear 1.112
## white 1.488
## structuraletiology 1.346
## priorepilepsy 1.613
## status 1.447
## ageyears 1.049
## SEXnumeric 1.471
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.286 0.265 8.616 0.000 9.832 5.846
## arm -0.010 0.183 -0.052 0.958 0.990 0.692
## TYPESTATUSnumeric -0.003 0.209 -0.012 0.990 0.997 0.662
## day 0.270 0.188 1.435 0.151 1.310 0.906
## earlyacademicyear 0.262 0.193 1.361 0.174 1.300 0.891
## white -0.131 0.189 -0.696 0.486 0.877 0.606
## structuraletiology 0.040 0.197 0.203 0.839 1.041 0.707
## priorepilepsy -0.223 0.234 -0.954 0.340 0.800 0.506
## status -0.011 0.275 -0.041 0.967 0.989 0.576
## ageyears -0.031 0.020 -1.520 0.129 0.970 0.932
## SEXnumeric -0.125 0.201 -0.621 0.534 0.882 0.595
## upper .95
## intercept 16.538
## arm 1.418
## TYPESTATUSnumeric 1.504
## day 1.894
## earlyacademicyear 1.895
## white 1.269
## structuraletiology 1.532
## priorepilepsy 1.265
## status 1.696
## ageyears 1.009
## SEXnumeric 1.309
# First BZD later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 69 | 15 |
## | 0.821 | 0.179 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 36 | 10 |
## | 0.783 | 0.217 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 38
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 33 | 5 |
## | 0.868 | 0.132 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore40min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore40min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.3956
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1328788 1.9896477
## sample estimates:
## odds ratio
## 0.5493172
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -1.965 -7.604 3.673 0.495
## RMST (arm=1)/(arm=0) 0.884 0.619 1.263 0.499
## RMTL (arm=1)/(arm=0) 1.085 0.855 1.376 0.503
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 15.388 4.701 3.273 0.001 6.175 24.601
## arm -1.965 2.877 -0.683 0.495 -7.604 3.673
## TYPESTATUSnumeric -3.274 3.031 -1.080 0.280 -9.214 2.666
## day -2.768 3.070 -0.902 0.367 -8.785 3.249
## earlyacademicyear -4.548 3.053 -1.490 0.136 -10.531 1.435
## white 2.015 3.316 0.608 0.543 -4.485 8.514
## structuraletiology 0.512 3.257 0.157 0.875 -5.871 6.895
## priorepilepsy 2.170 3.645 0.595 0.552 -4.975 9.315
## status -0.598 3.974 -0.150 0.880 -8.386 7.191
## ageyears 0.638 0.291 2.190 0.029 0.067 1.209
## SEXnumeric -1.607 3.093 -0.520 0.603 -7.670 4.455
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.702 0.315 8.590 0.000 14.909 8.048
## arm -0.123 0.182 -0.677 0.499 0.884 0.619
## TYPESTATUSnumeric -0.239 0.220 -1.090 0.276 0.787 0.512
## day -0.164 0.192 -0.858 0.391 0.848 0.583
## earlyacademicyear -0.296 0.200 -1.479 0.139 0.743 0.502
## white 0.123 0.215 0.572 0.568 1.131 0.742
## structuraletiology 0.055 0.208 0.266 0.791 1.057 0.702
## priorepilepsy 0.130 0.218 0.593 0.553 1.138 0.742
## status -0.005 0.231 -0.023 0.982 0.995 0.632
## ageyears 0.039 0.017 2.309 0.021 1.040 1.006
## SEXnumeric -0.136 0.202 -0.675 0.500 0.873 0.588
## upper .95
## intercept 27.619
## arm 1.263
## TYPESTATUSnumeric 1.210
## day 1.235
## earlyacademicyear 1.101
## white 1.723
## structuraletiology 1.590
## priorepilepsy 1.746
## status 1.565
## ageyears 1.074
## SEXnumeric 1.296
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.192 0.196 16.321 0.000 24.330 16.584
## arm 0.081 0.121 0.670 0.503 1.085 0.855
## TYPESTATUSnumeric 0.127 0.122 1.040 0.298 1.135 0.894
## day 0.120 0.132 0.910 0.363 1.127 0.871
## earlyacademicyear 0.187 0.129 1.447 0.148 1.205 0.936
## white -0.087 0.139 -0.622 0.534 0.917 0.698
## structuraletiology -0.011 0.136 -0.080 0.936 0.989 0.758
## priorepilepsy -0.094 0.163 -0.579 0.563 0.910 0.661
## status 0.037 0.180 0.205 0.837 1.038 0.729
## ageyears -0.027 0.014 -2.003 0.045 0.973 0.947
## SEXnumeric 0.051 0.129 0.392 0.695 1.052 0.817
## upper .95
## intercept 35.694
## arm 1.376
## TYPESTATUSnumeric 1.442
## day 1.459
## earlyacademicyear 1.553
## white 1.205
## structuraletiology 1.291
## priorepilepsy 1.252
## status 1.478
## ageyears 0.999
## SEXnumeric 1.354
# First BZD later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 73 | 11 |
## | 0.869 | 0.131 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 38 | 8 |
## | 0.826 | 0.174 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 38
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 35 | 3 |
## | 0.921 | 0.079 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.3304
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.06512687 1.88926369
## sample estimates:
## odds ratio
## 0.4112813
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -4.601 -12.197 2.996 0.235
## RMST (arm=1)/(arm=0) 0.768 0.515 1.146 0.196
## RMTL (arm=1)/(arm=0) 1.114 0.922 1.345 0.263
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 22.958 6.788 3.382 0.001 9.653 36.263
## arm -4.601 3.876 -1.187 0.235 -12.197 2.996
## TYPESTATUSnumeric -5.003 3.956 -1.265 0.206 -12.756 2.750
## day -3.051 4.262 -0.716 0.474 -11.405 5.303
## earlyacademicyear -8.663 4.234 -2.046 0.041 -16.962 -0.364
## white 1.102 4.779 0.231 0.818 -8.265 10.470
## structuraletiology 0.538 4.425 0.122 0.903 -8.134 9.210
## priorepilepsy 0.733 4.944 0.148 0.882 -8.957 10.423
## status -0.994 5.135 -0.194 0.847 -11.058 9.071
## ageyears 0.901 0.410 2.197 0.028 0.097 1.705
## SEXnumeric -4.543 4.379 -1.038 0.299 -13.125 4.038
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.118 0.354 8.798 0.000 22.598 11.283
## arm -0.263 0.204 -1.292 0.196 0.768 0.515
## TYPESTATUSnumeric -0.305 0.258 -1.183 0.237 0.737 0.444
## day -0.168 0.217 -0.773 0.439 0.845 0.552
## earlyacademicyear -0.486 0.226 -2.152 0.031 0.615 0.395
## white 0.052 0.248 0.210 0.834 1.054 0.648
## structuraletiology 0.071 0.242 0.293 0.770 1.073 0.668
## priorepilepsy 0.035 0.240 0.147 0.883 1.036 0.647
## status -0.022 0.264 -0.085 0.932 0.978 0.583
## ageyears 0.047 0.019 2.464 0.014 1.048 1.010
## SEXnumeric -0.289 0.235 -1.233 0.217 0.749 0.473
## upper .95
## intercept 45.261
## arm 1.146
## TYPESTATUSnumeric 1.222
## day 1.294
## earlyacademicyear 0.957
## white 1.714
## structuraletiology 1.723
## priorepilepsy 1.660
## status 1.639
## ageyears 1.087
## SEXnumeric 1.186
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.609 0.172 20.945 0.000 36.919 26.339
## arm 0.108 0.096 1.120 0.263 1.114 0.922
## TYPESTATUSnumeric 0.117 0.093 1.261 0.207 1.125 0.937
## day 0.073 0.108 0.677 0.498 1.076 0.871
## earlyacademicyear 0.209 0.109 1.917 0.055 1.232 0.995
## white -0.027 0.120 -0.226 0.821 0.973 0.770
## structuraletiology -0.004 0.108 -0.039 0.969 0.996 0.806
## priorepilepsy -0.018 0.127 -0.145 0.885 0.982 0.765
## status 0.028 0.131 0.215 0.830 1.029 0.796
## ageyears -0.022 0.011 -2.005 0.045 0.978 0.957
## SEXnumeric 0.101 0.109 0.928 0.353 1.106 0.894
## upper .95
## intercept 51.750
## arm 1.345
## TYPESTATUSnumeric 1.350
## day 1.329
## earlyacademicyear 1.525
## white 1.230
## structuraletiology 1.230
## priorepilepsy 1.260
## status 1.329
## ageyears 1.000
## SEXnumeric 1.369
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 43 | 41 |
## | 0.512 | 0.488 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 22 | 24 |
## | 0.478 | 0.522 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 38
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 21 | 17 |
## | 0.553 | 0.447 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore40min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore40min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.5192
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2865956 1.9147086
## sample estimates:
## odds ratio
## 0.7447229
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.301 -6.816 2.214 0.318
## RMST (arm=1)/(arm=0) 0.929 0.799 1.079 0.333
## RMTL (arm=1)/(arm=0) 1.301 0.808 2.092 0.279
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 29.471 3.554 8.291 0.000 22.505 36.438
## arm -2.301 2.304 -0.999 0.318 -6.816 2.214
## TYPESTATUSnumeric 1.486 2.962 0.502 0.616 -4.320 7.292
## day -4.597 2.600 -1.768 0.077 -9.694 0.499
## earlyacademicyear -0.398 2.702 -0.147 0.883 -5.693 4.897
## white 4.431 2.479 1.787 0.074 -0.428 9.290
## structuraletiology -1.674 2.498 -0.670 0.503 -6.569 3.221
## priorepilepsy 0.184 2.722 0.068 0.946 -5.151 5.520
## status -1.884 3.275 -0.575 0.565 -8.302 4.534
## ageyears 0.440 0.240 1.830 0.067 -0.031 0.912
## SEXnumeric -0.549 2.908 -0.189 0.850 -6.248 5.150
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.380 0.119 28.453 0.000 29.383 23.279
## arm -0.074 0.076 -0.968 0.333 0.929 0.799
## TYPESTATUSnumeric 0.051 0.098 0.521 0.602 1.052 0.869
## day -0.151 0.087 -1.733 0.083 0.859 0.724
## earlyacademicyear -0.011 0.091 -0.122 0.903 0.989 0.828
## white 0.146 0.085 1.712 0.087 1.157 0.979
## structuraletiology -0.056 0.085 -0.656 0.512 0.946 0.800
## priorepilepsy 0.004 0.086 0.042 0.967 1.004 0.848
## status -0.063 0.108 -0.582 0.561 0.939 0.759
## ageyears 0.014 0.008 1.803 0.071 1.014 0.999
## SEXnumeric -0.023 0.099 -0.231 0.817 0.977 0.806
## upper .95
## intercept 37.087
## arm 1.079
## TYPESTATUSnumeric 1.274
## day 1.020
## earlyacademicyear 1.182
## white 1.368
## structuraletiology 1.118
## priorepilepsy 1.188
## status 1.161
## ageyears 1.030
## SEXnumeric 1.186
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.337 0.381 6.135 0.000 10.347 4.905
## arm 0.263 0.243 1.083 0.279 1.301 0.808
## TYPESTATUSnumeric -0.138 0.322 -0.430 0.667 0.871 0.464
## day 0.494 0.285 1.735 0.083 1.639 0.938
## earlyacademicyear 0.056 0.268 0.207 0.836 1.057 0.625
## white -0.479 0.255 -1.873 0.061 0.620 0.376
## structuraletiology 0.177 0.242 0.728 0.466 1.193 0.742
## priorepilepsy -0.036 0.351 -0.102 0.919 0.965 0.485
## status 0.183 0.371 0.493 0.622 1.200 0.580
## ageyears -0.054 0.032 -1.680 0.093 0.948 0.891
## SEXnumeric -0.003 0.290 -0.010 0.992 0.997 0.564
## upper .95
## intercept 21.826
## arm 2.092
## TYPESTATUSnumeric 1.636
## day 2.865
## earlyacademicyear 1.788
## white 1.022
## structuraletiology 1.919
## priorepilepsy 1.919
## status 2.483
## ageyears 1.009
## SEXnumeric 1.762
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 56 | 28 |
## | 0.667 | 0.333 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 30 | 16 |
## | 0.652 | 0.348 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 38
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 26 | 12 |
## | 0.684 | 0.316 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.8188
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3115521 2.3697827
## sample estimates:
## odds ratio
## 0.8668741
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -4.858 -12.620 2.905 0.220
## RMST (arm=1)/(arm=0) 0.883 0.723 1.079 0.225
## RMTL (arm=1)/(arm=0) 1.272 0.869 1.862 0.217
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 38.973 6.187 6.299 0.000 26.847 51.100
## arm -4.858 3.961 -1.227 0.220 -12.620 2.905
## TYPESTATUSnumeric -1.363 5.040 -0.270 0.787 -11.240 8.515
## day -8.188 4.402 -1.860 0.063 -16.817 0.441
## earlyacademicyear 0.903 4.436 0.204 0.839 -7.790 9.597
## white 7.171 4.339 1.653 0.098 -1.333 15.675
## structuraletiology -5.841 4.077 -1.432 0.152 -13.832 2.151
## priorepilepsy -1.230 4.728 -0.260 0.795 -10.496 8.036
## status -3.019 5.345 -0.565 0.572 -13.494 7.457
## ageyears 0.732 0.392 1.867 0.062 -0.036 1.500
## SEXnumeric 1.358 4.655 0.292 0.771 -7.765 10.481
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.660 0.161 22.775 0.000 38.863 28.362
## arm -0.124 0.102 -1.214 0.225 0.883 0.723
## TYPESTATUSnumeric -0.035 0.133 -0.261 0.794 0.966 0.744
## day -0.213 0.116 -1.835 0.066 0.808 0.644
## earlyacademicyear 0.030 0.117 0.260 0.795 1.031 0.820
## white 0.187 0.118 1.587 0.113 1.206 0.957
## structuraletiology -0.160 0.111 -1.451 0.147 0.852 0.686
## priorepilepsy -0.037 0.118 -0.316 0.752 0.963 0.765
## status -0.086 0.141 -0.609 0.543 0.918 0.696
## ageyears 0.018 0.010 1.838 0.066 1.018 0.999
## SEXnumeric 0.030 0.122 0.243 0.808 1.030 0.812
## upper .95
## intercept 53.251
## arm 1.079
## TYPESTATUSnumeric 1.254
## day 1.015
## earlyacademicyear 1.295
## white 1.520
## structuraletiology 1.058
## priorepilepsy 1.214
## status 1.210
## ageyears 1.037
## SEXnumeric 1.307
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.042 0.300 10.154 0.000 20.938 11.641
## arm 0.240 0.195 1.236 0.217 1.272 0.869
## TYPESTATUSnumeric 0.067 0.233 0.289 0.772 1.070 0.677
## day 0.395 0.219 1.801 0.072 1.485 0.966
## earlyacademicyear -0.026 0.206 -0.127 0.899 0.974 0.651
## white -0.345 0.202 -1.710 0.087 0.708 0.476
## structuraletiology 0.257 0.188 1.369 0.171 1.293 0.895
## priorepilepsy 0.051 0.252 0.204 0.838 1.053 0.643
## status 0.110 0.259 0.427 0.670 1.117 0.672
## ageyears -0.041 0.023 -1.771 0.077 0.960 0.918
## SEXnumeric -0.092 0.222 -0.413 0.679 0.912 0.591
## upper .95
## intercept 37.663
## arm 1.862
## TYPESTATUSnumeric 1.689
## day 2.283
## earlyacademicyear 1.458
## white 1.052
## structuraletiology 1.869
## priorepilepsy 1.724
## status 1.856
## ageyears 1.004
## SEXnumeric 1.409
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 72 | 12 |
## | 0.857 | 0.143 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 46
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 39 | 7 |
## | 0.848 | 0.152 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 38
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 33 | 5 |
## | 0.868 | 0.132 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore120min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore120min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1924922 3.4343290
## sample estimates:
## odds ratio
## 0.8458499
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, tau=120,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -10.200 -25.148 4.747 0.181
## RMST (arm=1)/(arm=0) 0.820 0.616 1.092 0.175
## RMTL (arm=1)/(arm=0) 1.164 0.925 1.463 0.195
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 59.329 13.353 4.443 0.000 33.158 85.500
## arm -10.200 7.627 -1.337 0.181 -25.148 4.747
## TYPESTATUSnumeric -13.182 8.450 -1.560 0.119 -29.744 3.380
## day -14.829 8.489 -1.747 0.081 -31.468 1.810
## earlyacademicyear -2.747 8.331 -0.330 0.742 -19.076 13.582
## white 8.025 8.368 0.959 0.338 -8.375 24.425
## structuraletiology -13.232 7.606 -1.740 0.082 -28.140 1.676
## priorepilepsy -3.639 9.653 -0.377 0.706 -22.559 15.281
## status -2.954 10.507 -0.281 0.779 -23.548 17.641
## ageyears 1.573 0.761 2.067 0.039 0.081 3.064
## SEXnumeric 4.876 8.244 0.591 0.554 -11.282 21.033
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.089 0.250 16.363 0.000 59.654 36.556
## arm -0.198 0.146 -1.356 0.175 0.820 0.616
## TYPESTATUSnumeric -0.277 0.182 -1.523 0.128 0.758 0.530
## day -0.279 0.162 -1.715 0.086 0.757 0.551
## earlyacademicyear -0.039 0.164 -0.238 0.812 0.962 0.697
## white 0.153 0.167 0.913 0.361 1.165 0.839
## structuraletiology -0.276 0.159 -1.733 0.083 0.759 0.555
## priorepilepsy -0.088 0.182 -0.481 0.630 0.916 0.641
## status -0.062 0.205 -0.300 0.764 0.940 0.629
## ageyears 0.027 0.013 2.111 0.035 1.028 1.002
## SEXnumeric 0.080 0.155 0.518 0.604 1.084 0.800
## upper .95
## intercept 97.347
## arm 1.092
## TYPESTATUSnumeric 1.083
## day 1.041
## earlyacademicyear 1.326
## white 1.617
## structuraletiology 1.037
## priorepilepsy 1.310
## status 1.406
## ageyears 1.054
## SEXnumeric 1.468
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.110 0.204 20.172 0.000 60.956 40.887
## arm 0.152 0.117 1.297 0.195 1.164 0.925
## TYPESTATUSnumeric 0.185 0.119 1.546 0.122 1.203 0.952
## day 0.227 0.133 1.707 0.088 1.255 0.967
## earlyacademicyear 0.046 0.123 0.376 0.707 1.048 0.822
## white -0.124 0.124 -1.001 0.317 0.884 0.693
## structuraletiology 0.188 0.111 1.686 0.092 1.207 0.970
## priorepilepsy 0.047 0.150 0.314 0.753 1.048 0.782
## status 0.038 0.159 0.240 0.810 1.039 0.760
## ageyears -0.025 0.013 -1.940 0.052 0.975 0.950
## SEXnumeric -0.083 0.127 -0.657 0.511 0.920 0.718
## upper .95
## intercept 90.875
## arm 1.463
## TYPESTATUSnumeric 1.520
## day 1.628
## earlyacademicyear 1.334
## white 1.126
## structuraletiology 1.501
## priorepilepsy 1.406
## status 1.420
## ageyears 1.000
## SEXnumeric 1.179
# First CI later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 32 |
## | 0.086 | 0.914 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 20 |
## | 0.130 | 0.870 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstCImore60min)
##
## 0 1
## 0 12
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.5361
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2150344 Inf
## sample estimates:
## odds ratio
## Inf
# Difference adjusting for covariates within the first 500 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
## Warning in sqrt(diag(varbeta)): NaNs produced
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 4.352 -1.143 9.847000e+00 0.121
## RMST (arm=1)/(arm=0) 1.075 0.977 1.184000e+00 0.138
## RMTL (arm=1)/(arm=0) 0.000 0.000 4.298243e+20 0.481
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 57.378 3.764 15.243 0.000 50.000 64.756
## arm 4.352 2.804 1.552 0.121 -1.143 9.847
## TYPESTATUSnumeric 5.105 5.762 0.886 0.376 -6.189 16.399
## day -5.391 3.909 -1.379 0.168 -13.053 2.271
## earlyacademicyear -0.963 3.113 -0.309 0.757 -7.065 5.140
## white -3.018 4.000 -0.755 0.451 -10.857 4.821
## structuraletiology 1.900 4.455 0.426 0.670 -6.832 10.632
## priorepilepsy 2.839 3.624 0.783 0.433 -4.264 9.941
## status 3.638 4.845 0.751 0.453 -5.859 13.134
## ageyears 0.313 0.392 0.798 0.425 -0.456 1.082
## SEXnumeric -5.273 4.003 -1.317 0.188 -13.118 2.572
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.044 0.072 56.278 0.000 57.061 49.565
## arm 0.073 0.049 1.483 0.138 1.075 0.977
## TYPESTATUSnumeric 0.091 0.111 0.824 0.410 1.096 0.881
## day -0.099 0.078 -1.268 0.205 0.906 0.778
## earlyacademicyear -0.015 0.056 -0.270 0.787 0.985 0.882
## white -0.049 0.071 -0.685 0.493 0.952 0.828
## structuraletiology 0.033 0.081 0.411 0.681 1.034 0.882
## priorepilepsy 0.044 0.065 0.689 0.491 1.045 0.921
## status 0.068 0.093 0.731 0.465 1.070 0.892
## ageyears 0.006 0.008 0.765 0.444 1.006 0.991
## SEXnumeric -0.092 0.073 -1.251 0.211 0.913 0.791
## upper .95
## intercept 65.691
## arm 1.184
## TYPESTATUSnumeric 1.362
## day 1.055
## earlyacademicyear 1.100
## white 1.095
## structuraletiology 1.211
## priorepilepsy 1.186
## status 1.283
## ageyears 1.021
## SEXnumeric 1.053
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef)
## intercept -99.156 NaN NaN NaN 0.000000e+00
## arm -26.649 37.837 -0.704 0.481 0.000000e+00
## TYPESTATUSnumeric -18.133 1.285 -14.107 0.000 0.000000e+00
## day 35.764 NaN NaN NaN 3.406322e+15
## earlyacademicyear 40.975 0.727 56.339 0.000 6.237754e+17
## white 40.774 NaN NaN NaN 5.102771e+17
## structuraletiology -2.403 NaN NaN NaN 9.000000e-02
## priorepilepsy -38.782 1.902 -20.389 0.000 0.000000e+00
## status -28.478 4.008 -7.105 0.000 0.000000e+00
## ageyears 0.242 0.152 1.597 0.110 1.274000e+00
## SEXnumeric 26.080 2.263 11.525 0.000 2.120004e+11
## lower .95 upper .95
## intercept NaN NaN
## arm 0.000000e+00 4.298243e+20
## TYPESTATUSnumeric 0.000000e+00 0.000000e+00
## day NaN NaN
## earlyacademicyear 1.499545e+17 2.594758e+18
## white NaN NaN
## structuraletiology NaN NaN
## priorepilepsy 0.000000e+00 0.000000e+00
## status 0.000000e+00 0.000000e+00
## ageyears 9.460000e-01 1.716000e+00
## SEXnumeric 2.512447e+09 1.788860e+13
# First CI later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 8 | 27 |
## | 0.229 | 0.771 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 17 |
## | 0.261 | 0.739 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 12
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 2 | 10 |
## | 0.167 | 0.833 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore120min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore120min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 0.6855
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2442521 20.8287560
## sample estimates:
## odds ratio
## 1.737725
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
## Warning in sqrt(diag(varbeta)): NaNs produced
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 11.303 -2.555 25.161 0.110
## RMST (arm=1)/(arm=0) 1.102 0.970 1.252 0.134
## RMTL (arm=1)/(arm=0) 0.000 0.000 0.000 0.000
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 116.199 8.464 13.729 0.000 99.610 132.787
## arm 11.303 7.071 1.599 0.110 -2.555 25.161
## TYPESTATUSnumeric 8.469 13.950 0.607 0.544 -18.872 35.811
## day -12.787 9.042 -1.414 0.157 -30.510 4.936
## earlyacademicyear -4.477 7.892 -0.567 0.571 -19.946 10.992
## white -6.028 9.599 -0.628 0.530 -24.841 12.785
## structuraletiology 1.246 10.796 0.115 0.908 -19.913 22.405
## priorepilepsy 8.520 8.828 0.965 0.334 -8.782 25.822
## status 7.094 11.449 0.620 0.536 -15.346 29.534
## ageyears 0.495 0.865 0.573 0.567 -1.199 2.190
## SEXnumeric -11.996 8.920 -1.345 0.179 -29.479 5.487
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.747 0.081 58.407 0.000 115.229 98.261
## arm 0.097 0.065 1.497 0.134 1.102 0.970
## TYPESTATUSnumeric 0.075 0.136 0.551 0.582 1.078 0.825
## day -0.118 0.091 -1.291 0.197 0.889 0.743
## earlyacademicyear -0.038 0.073 -0.520 0.603 0.963 0.835
## white -0.047 0.089 -0.525 0.600 0.954 0.801
## structuraletiology 0.010 0.100 0.105 0.917 1.011 0.831
## priorepilepsy 0.069 0.082 0.838 0.402 1.071 0.912
## status 0.068 0.114 0.596 0.551 1.070 0.856
## ageyears 0.005 0.008 0.568 0.570 1.005 0.988
## SEXnumeric -0.106 0.084 -1.261 0.207 0.899 0.763
## upper .95
## intercept 135.127
## arm 1.252
## TYPESTATUSnumeric 1.408
## day 1.063
## earlyacademicyear 1.110
## white 1.137
## structuraletiology 1.229
## priorepilepsy 1.259
## status 1.339
## ageyears 1.022
## SEXnumeric 1.061
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef)
## intercept -175.444 NaN NaN NaN 0.000000e+00
## arm -23.707 1.243 -19.072 0.000 0.000000e+00
## TYPESTATUSnumeric -18.407 NaN NaN NaN 0.000000e+00
## day 77.620 67.182 1.155 0.248 5.125761e+33
## earlyacademicyear 39.307 NaN NaN NaN 1.177384e+17
## white 78.609 NaN NaN NaN 1.378835e+34
## structuraletiology 37.720 NaN NaN NaN 2.407827e+16
## priorepilepsy -40.544 NaN NaN NaN 0.000000e+00
## status -24.277 3.588 -6.766 0.000 0.000000e+00
## ageyears 0.203 0.141 1.438 0.151 1.225000e+00
## SEXnumeric 23.504 1.773 13.259 0.000 1.612335e+10
## lower .95 upper .95
## intercept NaN NaN
## arm 0.000000e+00 0.000000e+00
## TYPESTATUSnumeric NaN NaN
## day 0.000000e+00 7.854917e+90
## earlyacademicyear NaN NaN
## white NaN NaN
## structuraletiology NaN NaN
## priorepilepsy NaN NaN
## status 0.000000e+00 0.000000e+00
## ageyears 9.290000e-01 1.616000e+00
## SEXnumeric 4.995373e+08 5.204063e+11
# First CI later than 240 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 15 |
## | 0.571 | 0.429 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 23
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 13 | 10 |
## | 0.565 | 0.435 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness == 1, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 12
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 7 | 5 |
## | 0.583 | 0.417 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore240min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore240min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1751422 4.6851010
## sample estimates:
## odds ratio
## 0.9305588
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 1.591 -40.905 44.087 0.942
## RMST (arm=1)/(arm=0) 1.005 0.797 1.267 0.965
## RMTL (arm=1)/(arm=0) 0.944 0.422 2.113 0.889
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 210.704 30.449 6.920 0.000 151.024 270.383
## arm 1.591 21.682 0.073 0.942 -40.905 44.087
## TYPESTATUSnumeric -7.301 32.284 -0.226 0.821 -70.576 55.974
## day -23.295 22.270 -1.046 0.296 -66.943 20.354
## earlyacademicyear -19.248 24.487 -0.786 0.432 -67.242 28.746
## white -20.292 26.931 -0.753 0.451 -73.077 32.493
## structuraletiology 25.386 28.189 0.901 0.368 -29.863 80.636
## priorepilepsy -14.580 27.204 -0.536 0.592 -67.900 38.739
## status 17.075 37.019 0.461 0.645 -55.481 89.631
## ageyears 2.585 1.806 1.432 0.152 -0.954 6.125
## SEXnumeric -39.318 24.585 -1.599 0.110 -87.504 8.867
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.346 0.169 31.692 0.000 209.810 150.742
## arm 0.005 0.118 0.043 0.965 1.005 0.797
## TYPESTATUSnumeric -0.036 0.201 -0.176 0.860 0.965 0.650
## day -0.136 0.130 -1.049 0.294 0.873 0.677
## earlyacademicyear -0.105 0.137 -0.768 0.443 0.900 0.688
## white -0.104 0.151 -0.690 0.490 0.901 0.670
## structuraletiology 0.138 0.156 0.885 0.376 1.148 0.846
## priorepilepsy -0.078 0.159 -0.493 0.622 0.925 0.677
## status 0.092 0.216 0.427 0.669 1.097 0.718
## ageyears 0.014 0.010 1.371 0.170 1.014 0.994
## SEXnumeric -0.209 0.141 -1.479 0.139 0.811 0.615
## upper .95
## intercept 292.023
## arm 1.267
## TYPESTATUSnumeric 1.432
## day 1.125
## earlyacademicyear 1.178
## white 1.212
## structuraletiology 1.557
## priorepilepsy 1.263
## status 1.675
## ageyears 1.036
## SEXnumeric 1.070
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.317 0.609 5.444 0.000 27.576 8.354
## arm -0.058 0.411 -0.140 0.889 0.944 0.422
## TYPESTATUSnumeric 0.204 0.442 0.460 0.645 1.226 0.515
## day 0.346 0.438 0.791 0.429 1.414 0.599
## earlyacademicyear 0.368 0.438 0.842 0.400 1.445 0.613
## white 0.465 0.502 0.926 0.354 1.592 0.595
## structuraletiology -0.471 0.534 -0.881 0.378 0.625 0.219
## priorepilepsy 0.310 0.425 0.729 0.466 1.363 0.593
## status -0.339 0.613 -0.553 0.581 0.713 0.215
## ageyears -0.047 0.034 -1.383 0.167 0.954 0.892
## SEXnumeric 0.823 0.459 1.791 0.073 2.277 0.925
## upper .95
## intercept 91.024
## arm 2.113
## TYPESTATUSnumeric 2.917
## day 3.337
## earlyacademicyear 3.408
## white 4.262
## structuraletiology 1.779
## priorepilepsy 3.137
## status 2.369
## ageyears 1.020
## SEXnumeric 5.601
Time to treatment sensitivity analysis 3: Only initial centers and threshold in 2017
# Distribution of patients according to the threhold
CrossTable(pSERG$awareness2017)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 220 | 48 |
## | 0.821 | 0.179 |
## |-----------|-----------|
##
##
##
##
## ALL PATIENTS
# Time to first BZD
summary(pSERG$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 20.00 56.67 45.00 1264.00
sd(pSERG$BZDTIME.0)
## [1] 134.0915
survfit(Surv(pSERG$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$BZDTIME.0) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## 268 268 20 15 23
# Figure time to first BZD
plot(survfit(Surv(pSERG$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$awareness2017 == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 5.00 20.00 59.83 49.25 1264.00
summary(pSERG[which(pSERG$awareness2017 == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 5.00 20.00 42.19 33.50 330.00
survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho = 1)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 150 76.5 80.6 0.207 0.763
## pSERG$awareness=1 118 63.2 59.1 0.283 0.763
##
## Chisq= 0.8 on 1 degrees of freedom, p= 0.4
pchisq(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.3824629
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG$BZDTIME.0) ~ pSERG$awareness2017 + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$BZDTIME.0) ~ pSERG$awareness2017 +
## pSERG$TYPESTATUS + pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 268, number of events= 268
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness2017 0.075029 1.077915 0.163444 0.459 0.646200
## pSERG$TYPESTATUSintermittent -0.384395 0.680862 0.139573 -2.754 0.005886
## pSERG$HOSPITALONSETyes 0.529355 1.697836 0.141435 3.743 0.000182
## pSERG$day 0.064426 1.066547 0.128450 0.502 0.615973
## pSERG$earlyacademicyear 0.226337 1.253999 0.125268 1.807 0.070790
## pSERG$white 0.092024 1.096391 0.132697 0.693 0.488002
## pSERG$structuraletiology 0.059093 1.060874 0.148161 0.399 0.690008
## pSERG$priorepilepsy 0.021418 1.021649 0.138026 0.155 0.876686
## pSERG$status 0.381622 1.464658 0.172735 2.209 0.027154
## pSERG$ageyears -0.003457 0.996549 0.012385 -0.279 0.780151
## pSERG$SEXmale 0.059181 1.060967 0.127526 0.464 0.642598
##
## pSERG$awareness2017
## pSERG$TYPESTATUSintermittent **
## pSERG$HOSPITALONSETyes ***
## pSERG$day
## pSERG$earlyacademicyear .
## pSERG$white
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status *
## pSERG$ageyears
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness2017 1.0779 0.9277 0.7825 1.4849
## pSERG$TYPESTATUSintermittent 0.6809 1.4687 0.5179 0.8951
## pSERG$HOSPITALONSETyes 1.6978 0.5890 1.2868 2.2402
## pSERG$day 1.0665 0.9376 0.8292 1.3719
## pSERG$earlyacademicyear 1.2540 0.7974 0.9810 1.6030
## pSERG$white 1.0964 0.9121 0.8453 1.4221
## pSERG$structuraletiology 1.0609 0.9426 0.7935 1.4183
## pSERG$priorepilepsy 1.0216 0.9788 0.7795 1.3390
## pSERG$status 1.4647 0.6828 1.0440 2.0548
## pSERG$ageyears 0.9965 1.0035 0.9727 1.0210
## pSERG$SEXmale 1.0610 0.9425 0.8263 1.3622
##
## Concordance= 0.617 (se = 0.022 )
## Rsquare= 0.116 (max possible= 1 )
## Likelihood ratio test= 33.11 on 11 df, p=5e-04
## Wald test = 34.69 on 11 df, p=3e-04
## Score (logrank) test = 35.35 on 11 df, p=2e-04
# Time to first non-BZD AED
summary(pSERG$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 35.0 65.5 153.2 150.8 1800.0
sd(pSERG$AEDTIME.0)
## [1] 246.9072
survfit(Surv(pSERG$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$AEDTIME.0) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## 268.0 268.0 65.5 60.0 77.0
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$awareness2017 == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 35.75 66.50 152.77 150.75 1800.00
summary(pSERG[which(pSERG$awareness2017 == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 31.75 64.50 155.21 159.25 1419.00
survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho = 1)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 150 76.0 75.7 0.00127 0.00433
## pSERG$awareness=1 118 59.5 59.8 0.00160 0.00433
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
pchisq(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.9475229
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG$AEDTIME.0) ~ pSERG$awareness2017 + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$AEDTIME.0) ~ pSERG$awareness2017 +
## pSERG$TYPESTATUS + pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 268, number of events= 268
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness2017 -0.07470 0.92802 0.16235 -0.460 0.6454
## pSERG$TYPESTATUSintermittent -0.55762 0.57257 0.13938 -4.001 6.32e-05
## pSERG$HOSPITALONSETyes 0.86510 2.37524 0.14263 6.065 1.32e-09
## pSERG$day 0.25788 1.29419 0.13050 1.976 0.0481
## pSERG$earlyacademicyear 0.10822 1.11429 0.12518 0.865 0.3873
## pSERG$white 0.03414 1.03473 0.12865 0.265 0.7907
## pSERG$structuraletiology 0.18185 1.19943 0.14549 1.250 0.2113
## pSERG$priorepilepsy 0.08628 1.09012 0.14152 0.610 0.5421
## pSERG$status 0.20246 1.22441 0.17271 1.172 0.2411
## pSERG$ageyears -0.02791 0.97247 0.01225 -2.279 0.0227
## pSERG$SEXmale 0.05115 1.05248 0.12971 0.394 0.6933
##
## pSERG$awareness2017
## pSERG$TYPESTATUSintermittent ***
## pSERG$HOSPITALONSETyes ***
## pSERG$day *
## pSERG$earlyacademicyear
## pSERG$white
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status
## pSERG$ageyears *
## pSERG$SEXmale
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness2017 0.9280 1.0776 0.6751 1.2757
## pSERG$TYPESTATUSintermittent 0.5726 1.7465 0.4357 0.7524
## pSERG$HOSPITALONSETyes 2.3752 0.4210 1.7960 3.1413
## pSERG$day 1.2942 0.7727 1.0021 1.6714
## pSERG$earlyacademicyear 1.1143 0.8974 0.8719 1.4241
## pSERG$white 1.0347 0.9664 0.8041 1.3315
## pSERG$structuraletiology 1.1994 0.8337 0.9018 1.5952
## pSERG$priorepilepsy 1.0901 0.9173 0.8261 1.4386
## pSERG$status 1.2244 0.8167 0.8728 1.7177
## pSERG$ageyears 0.9725 1.0283 0.9494 0.9961
## pSERG$SEXmale 1.0525 0.9501 0.8162 1.3571
##
## Concordance= 0.648 (se = 0.021 )
## Rsquare= 0.192 (max possible= 1 )
## Likelihood ratio test= 57.08 on 11 df, p=3e-08
## Wald test = 59.4 on 11 df, p=1e-08
## Score (logrank) test = 60.96 on 11 df, p=6e-09
# Time to first CI
summary(pSERG$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 122.0 206.0 523.7 612.5 7200.0 149
sd(pSERG$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG$CONTTIME.0) ~ 1)
##
## 149 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 119 119 206 165 300
# Figure time to first CI
plot(survfit(Surv(pSERG$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$awareness2017 == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 122.0 186.0 518.0 574.8 7200.0 114
summary(pSERG[which(pSERG$awareness2017 == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 75.0 135.0 420.0 569.5 1000.0 1435.0 35
survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG$CONTTIME.0) ~ pSERG$awareness,
## rho = 1)
##
## n=119, 149 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## pSERG$awareness=0 67 34.5 33.3 0.0440 0.148
## pSERG$awareness=1 52 25.7 26.9 0.0544 0.148
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.7
pchisq(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.7006702
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG$CONTTIME.0) ~ pSERG$awareness2017 + pSERG$TYPESTATUS + pSERG$HOSPITALONSET +
pSERG$day + pSERG$earlyacademicyear + pSERG$white +
pSERG$structuraletiology + pSERG$priorepilepsy +
pSERG$status + pSERG$ageyears + pSERG$SEX))
## Call:
## coxph(formula = Surv(pSERG$CONTTIME.0) ~ pSERG$awareness2017 +
## pSERG$TYPESTATUS + pSERG$HOSPITALONSET + pSERG$day + pSERG$earlyacademicyear +
## pSERG$white + pSERG$structuraletiology + pSERG$priorepilepsy +
## pSERG$status + pSERG$ageyears + pSERG$SEX)
##
## n= 119, number of events= 119
## (149 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pSERG$awareness2017 -0.203146 0.816159 0.311332 -0.653 0.5141
## pSERG$TYPESTATUSintermittent -0.211139 0.809662 0.219835 -0.960 0.3368
## pSERG$HOSPITALONSETyes 0.098984 1.104049 0.223892 0.442 0.6584
## pSERG$day 0.014998 1.015111 0.194558 0.077 0.9386
## pSERG$earlyacademicyear 0.478583 1.613787 0.203825 2.348 0.0189
## pSERG$white -0.492579 0.611049 0.214652 -2.295 0.0217
## pSERG$structuraletiology 0.141753 1.152292 0.239776 0.591 0.5544
## pSERG$priorepilepsy 0.162706 1.176691 0.247746 0.657 0.5113
## pSERG$status 0.107867 1.113899 0.275936 0.391 0.6959
## pSERG$ageyears -0.001253 0.998748 0.019789 -0.063 0.9495
## pSERG$SEXmale 0.363517 1.438379 0.200184 1.816 0.0694
##
## pSERG$awareness2017
## pSERG$TYPESTATUSintermittent
## pSERG$HOSPITALONSETyes
## pSERG$day
## pSERG$earlyacademicyear *
## pSERG$white *
## pSERG$structuraletiology
## pSERG$priorepilepsy
## pSERG$status
## pSERG$ageyears
## pSERG$SEXmale .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pSERG$awareness2017 0.8162 1.2253 0.4434 1.5024
## pSERG$TYPESTATUSintermittent 0.8097 1.2351 0.5262 1.2457
## pSERG$HOSPITALONSETyes 1.1040 0.9058 0.7119 1.7122
## pSERG$day 1.0151 0.9851 0.6933 1.4864
## pSERG$earlyacademicyear 1.6138 0.6197 1.0823 2.4063
## pSERG$white 0.6110 1.6365 0.4012 0.9307
## pSERG$structuraletiology 1.1523 0.8678 0.7202 1.8436
## pSERG$priorepilepsy 1.1767 0.8498 0.7241 1.9122
## pSERG$status 1.1139 0.8977 0.6486 1.9130
## pSERG$ageyears 0.9987 1.0013 0.9608 1.0382
## pSERG$SEXmale 1.4384 0.6952 0.9716 2.1295
##
## Concordance= 0.592 (se = 0.031 )
## Rsquare= 0.118 (max possible= 1 )
## Likelihood ratio test= 14.93 on 11 df, p=0.2
## Wald test = 14.95 on 11 df, p=0.2
## Score (logrank) test = 15.09 on 11 df, p=0.2
# First BZD later than 20 minutes
CrossTable(pSERG$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 149 | 119 |
## | 0.556 | 0.444 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 220
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 121 | 99 |
## | 0.550 | 0.450 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 48
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 20 |
## | 0.583 | 0.417 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore20min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore20min and pSERG$awareness2017
## p-value = 0.7494
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4378284 1.7172603
## sample estimates:
## odds ratio
## 0.8734567
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=20,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.126 -2.137 2.388 0.913
## RMST (arm=1)/(arm=0) 1.013 0.857 1.196 0.883
## RMTL (arm=1)/(arm=0) 0.992 0.698 1.411 0.966
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 16.322 1.354 12.051 0.000 13.668 18.977
## arm 0.126 1.154 0.109 0.913 -2.137 2.388
## TYPESTATUSnumeric -0.473 0.902 -0.524 0.600 -2.242 1.296
## HOSPITALONSETnumeric -3.504 0.988 -3.545 0.000 -5.442 -1.567
## day -0.629 0.857 -0.734 0.463 -2.309 1.051
## earlyacademicyear -0.944 0.865 -1.091 0.275 -2.640 0.752
## white -0.252 0.861 -0.292 0.770 -1.939 1.436
## structuraletiology -0.494 1.011 -0.489 0.625 -2.476 1.487
## priorepilepsy -0.415 0.895 -0.464 0.643 -2.170 1.340
## status -2.786 1.267 -2.198 0.028 -5.270 -0.302
## ageyears 0.010 0.088 0.115 0.908 -0.163 0.183
## SEXnumeric 0.550 0.874 0.630 0.529 -1.162 2.263
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.808 0.099 28.361 0.000 16.571 13.649
## arm 0.013 0.085 0.147 0.883 1.013 0.857
## TYPESTATUSnumeric -0.032 0.067 -0.481 0.630 0.968 0.849
## HOSPITALONSETnumeric -0.276 0.083 -3.330 0.001 0.759 0.645
## day -0.049 0.063 -0.788 0.431 0.952 0.842
## earlyacademicyear -0.070 0.064 -1.103 0.270 0.932 0.823
## white -0.020 0.063 -0.322 0.747 0.980 0.866
## structuraletiology -0.037 0.076 -0.489 0.625 0.964 0.831
## priorepilepsy -0.034 0.064 -0.536 0.592 0.966 0.853
## status -0.225 0.108 -2.085 0.037 0.799 0.647
## ageyears 0.001 0.006 0.141 0.888 1.001 0.988
## SEXnumeric 0.041 0.064 0.638 0.524 1.042 0.919
## upper .95
## intercept 20.120
## arm 1.196
## TYPESTATUSnumeric 1.104
## HOSPITALONSETnumeric 0.893
## day 1.076
## earlyacademicyear 1.056
## white 1.109
## structuraletiology 1.118
## priorepilepsy 1.095
## status 0.987
## ageyears 1.014
## SEXnumeric 1.181
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.413 0.221 6.396 0.000 4.109 2.665
## arm -0.008 0.180 -0.042 0.966 0.992 0.698
## TYPESTATUSnumeric 0.088 0.140 0.630 0.528 1.092 0.830
## HOSPITALONSETnumeric 0.499 0.140 3.574 0.000 1.648 1.253
## day 0.085 0.139 0.614 0.539 1.089 0.830
## earlyacademicyear 0.146 0.138 1.053 0.293 1.157 0.882
## white 0.032 0.137 0.234 0.815 1.033 0.790
## structuraletiology 0.075 0.154 0.485 0.627 1.078 0.797
## priorepilepsy 0.048 0.153 0.315 0.752 1.050 0.777
## status 0.379 0.170 2.233 0.026 1.461 1.047
## ageyears -0.001 0.014 -0.058 0.954 0.999 0.972
## SEXnumeric -0.085 0.140 -0.607 0.544 0.919 0.698
## upper .95
## intercept 6.336
## arm 1.411
## TYPESTATUSnumeric 1.438
## HOSPITALONSETnumeric 2.167
## day 1.428
## earlyacademicyear 1.518
## white 1.350
## structuraletiology 1.457
## priorepilepsy 1.417
## status 2.037
## ageyears 1.027
## SEXnumeric 1.209
# First BZD later than 40 minutes
CrossTable(pSERG$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 192 | 76 |
## | 0.716 | 0.284 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 220
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 154 | 66 |
## | 0.700 | 0.300 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 48
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 38 | 10 |
## | 0.792 | 0.208 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore40min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore40min and pSERG$awareness2017
## p-value = 0.2213
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2576377 1.3520217
## sample estimates:
## odds ratio
## 0.6150764
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=40,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.838 -5.315 3.638 0.714
## RMST (arm=1)/(arm=0) 0.967 0.772 1.212 0.771
## RMTL (arm=1)/(arm=0) 1.052 0.842 1.315 0.656
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 26.049 2.728 9.549 0.000 20.703 31.396
## arm -0.838 2.284 -0.367 0.714 -5.315 3.638
## TYPESTATUSnumeric -3.575 1.751 -2.042 0.041 -7.007 -0.143
## HOSPITALONSETnumeric -7.722 1.877 -4.115 0.000 -11.400 -4.044
## day -0.582 1.775 -0.328 0.743 -4.061 2.898
## earlyacademicyear -2.010 1.752 -1.147 0.251 -5.443 1.424
## white 0.356 1.765 0.202 0.840 -3.104 3.816
## structuraletiology 0.529 2.091 0.253 0.800 -3.569 4.627
## priorepilepsy 0.687 1.877 0.366 0.714 -2.991 4.365
## status -5.972 2.463 -2.424 0.015 -10.800 -1.144
## ageyears 0.047 0.178 0.267 0.790 -0.302 0.397
## SEXnumeric -0.158 1.776 -0.089 0.929 -3.638 3.323
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.277 0.130 25.164 0.000 26.508 20.536
## arm -0.034 0.115 -0.291 0.771 0.967 0.772
## TYPESTATUSnumeric -0.171 0.089 -1.926 0.054 0.843 0.708
## HOSPITALONSETnumeric -0.403 0.108 -3.738 0.000 0.668 0.541
## day -0.032 0.085 -0.377 0.706 0.969 0.820
## earlyacademicyear -0.098 0.085 -1.154 0.248 0.907 0.768
## white 0.015 0.085 0.180 0.857 1.016 0.859
## structuraletiology 0.027 0.100 0.266 0.790 1.027 0.844
## priorepilepsy 0.024 0.086 0.284 0.777 1.025 0.865
## status -0.314 0.144 -2.182 0.029 0.731 0.551
## ageyears 0.002 0.008 0.283 0.777 1.002 0.986
## SEXnumeric -0.004 0.085 -0.050 0.960 0.996 0.843
## upper .95
## intercept 34.217
## arm 1.212
## TYPESTATUSnumeric 1.003
## HOSPITALONSETnumeric 0.825
## day 1.144
## earlyacademicyear 1.071
## white 1.201
## structuraletiology 1.249
## priorepilepsy 1.213
## status 0.969
## ageyears 1.019
## SEXnumeric 1.176
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.667 0.147 18.171 0.000 14.402 10.802
## arm 0.051 0.114 0.445 0.656 1.052 0.842
## TYPESTATUSnumeric 0.189 0.089 2.128 0.033 1.208 1.015
## HOSPITALONSETnumeric 0.381 0.092 4.158 0.000 1.463 1.223
## day 0.026 0.095 0.276 0.782 1.026 0.853
## earlyacademicyear 0.104 0.092 1.132 0.258 1.110 0.926
## white -0.021 0.092 -0.230 0.818 0.979 0.817
## structuraletiology -0.027 0.110 -0.243 0.808 0.974 0.784
## priorepilepsy -0.046 0.105 -0.442 0.659 0.955 0.778
## status 0.295 0.116 2.538 0.011 1.344 1.070
## ageyears -0.002 0.010 -0.253 0.801 0.998 0.979
## SEXnumeric 0.011 0.094 0.114 0.909 1.011 0.841
## upper .95
## intercept 19.204
## arm 1.315
## TYPESTATUSnumeric 1.437
## HOSPITALONSETnumeric 1.751
## day 1.236
## earlyacademicyear 1.330
## white 1.173
## structuraletiology 1.209
## priorepilepsy 1.172
## status 1.688
## ageyears 1.016
## SEXnumeric 1.215
# First BZD later than 60 minutes
CrossTable(pSERG$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 217 | 51 |
## | 0.810 | 0.190 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 220
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 175 | 45 |
## | 0.795 | 0.205 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 48
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 42 | 6 |
## | 0.875 | 0.125 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstBZDmore60min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstBZDmore60min and pSERG$awareness2017
## p-value = 0.2302
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1819544 1.4306439
## sample estimates:
## odds ratio
## 0.5566328
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG$BZDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=60,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.503 -8.719 3.713 0.430
## RMST (arm=1)/(arm=0) 0.914 0.701 1.191 0.506
## RMTL (arm=1)/(arm=0) 1.080 0.909 1.282 0.381
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.558 3.998 8.645 0.000 26.723 42.393
## arm -2.503 3.171 -0.789 0.430 -8.719 3.713
## TYPESTATUSnumeric -6.420 2.508 -2.560 0.010 -11.336 -1.505
## HOSPITALONSETnumeric -10.580 2.648 -3.995 0.000 -15.771 -5.390
## day -0.746 2.590 -0.288 0.773 -5.822 4.330
## earlyacademicyear -3.514 2.532 -1.388 0.165 -8.478 1.450
## white -0.230 2.604 -0.088 0.930 -5.333 4.873
## structuraletiology 0.827 2.990 0.277 0.782 -5.034 6.688
## priorepilepsy 2.400 2.749 0.873 0.383 -2.987 7.788
## status -9.888 3.304 -2.993 0.003 -16.363 -3.413
## ageyears 0.032 0.255 0.125 0.900 -0.467 0.531
## SEXnumeric -1.219 2.570 -0.474 0.635 -6.256 3.819
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.577 0.152 23.544 0.000 35.752 26.546
## arm -0.090 0.135 -0.665 0.506 0.914 0.701
## TYPESTATUSnumeric -0.254 0.106 -2.387 0.017 0.776 0.630
## HOSPITALONSETnumeric -0.453 0.126 -3.589 0.000 0.636 0.496
## day -0.035 0.100 -0.354 0.724 0.965 0.793
## earlyacademicyear -0.137 0.099 -1.379 0.168 0.872 0.717
## white -0.010 0.102 -0.101 0.919 0.990 0.811
## structuraletiology 0.033 0.115 0.282 0.778 1.033 0.824
## priorepilepsy 0.080 0.101 0.787 0.431 1.083 0.888
## status -0.428 0.164 -2.614 0.009 0.652 0.473
## ageyears 0.001 0.010 0.116 0.907 1.001 0.982
## SEXnumeric -0.042 0.099 -0.422 0.673 0.959 0.789
## upper .95
## intercept 48.152
## arm 1.191
## TYPESTATUSnumeric 0.956
## HOSPITALONSETnumeric 0.814
## day 1.174
## earlyacademicyear 1.059
## white 1.208
## structuraletiology 1.295
## priorepilepsy 1.321
## status 0.898
## ageyears 1.020
## SEXnumeric 1.165
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.267 0.121 26.910 0.000 26.232 20.677
## arm 0.077 0.088 0.876 0.381 1.080 0.909
## TYPESTATUSnumeric 0.187 0.071 2.619 0.009 1.205 1.048
## HOSPITALONSETnumeric 0.295 0.073 4.022 0.000 1.343 1.163
## day 0.019 0.077 0.241 0.810 1.019 0.876
## earlyacademicyear 0.103 0.075 1.383 0.167 1.109 0.958
## white 0.005 0.076 0.070 0.944 1.005 0.866
## structuraletiology -0.024 0.089 -0.275 0.783 0.976 0.821
## priorepilepsy -0.078 0.085 -0.919 0.358 0.925 0.783
## status 0.277 0.090 3.071 0.002 1.320 1.106
## ageyears -0.001 0.008 -0.134 0.893 0.999 0.984
## SEXnumeric 0.038 0.076 0.495 0.621 1.038 0.895
## upper .95
## intercept 33.279
## arm 1.282
## TYPESTATUSnumeric 1.386
## HOSPITALONSETnumeric 1.551
## day 1.185
## earlyacademicyear 1.283
## white 1.168
## structuraletiology 1.161
## priorepilepsy 1.092
## status 1.575
## ageyears 1.014
## SEXnumeric 1.205
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 76 | 192 |
## | 0.284 | 0.716 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 150
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 45 | 105 |
## | 0.300 | 0.700 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 118
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 31 | 87 |
## | 0.263 | 0.737 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore40min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore40min and pSERG$awareness
## p-value = 0.5853
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6792881 2.1444062
## sample estimates:
## odds ratio
## 1.201938
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=40,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.885 -1.523 3.293 0.471
## RMST (arm=1)/(arm=0) 1.026 0.959 1.099 0.457
## RMTL (arm=1)/(arm=0) 0.872 0.517 1.471 0.607
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.770 1.897 18.330 0.000 31.052 38.488
## arm 0.885 1.229 0.720 0.471 -1.523 3.293
## TYPESTATUSnumeric -0.077 1.116 -0.069 0.945 -2.265 2.111
## HOSPITALONSETnumeric -6.983 1.415 -4.934 0.000 -9.757 -4.209
## day -1.489 1.110 -1.341 0.180 -3.665 0.688
## earlyacademicyear 0.940 1.083 0.869 0.385 -1.182 3.062
## white 1.661 1.095 1.518 0.129 -0.484 3.807
## structuraletiology -0.219 1.212 -0.181 0.856 -2.594 2.156
## priorepilepsy 1.204 1.116 1.079 0.281 -0.984 3.392
## status -0.630 1.372 -0.459 0.646 -3.319 2.060
## ageyears 0.173 0.112 1.546 0.122 -0.046 0.391
## SEXnumeric 0.811 1.137 0.713 0.476 -1.418 3.040
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.546 0.055 64.398 0.000 34.657 31.112
## arm 0.026 0.035 0.745 0.457 1.026 0.959
## TYPESTATUSnumeric -0.002 0.032 -0.057 0.955 0.998 0.938
## HOSPITALONSETnumeric -0.206 0.045 -4.585 0.000 0.813 0.745
## day -0.041 0.032 -1.309 0.191 0.960 0.902
## earlyacademicyear 0.026 0.031 0.850 0.395 1.027 0.966
## white 0.047 0.032 1.484 0.138 1.048 0.985
## structuraletiology -0.006 0.035 -0.165 0.869 0.994 0.928
## priorepilepsy 0.034 0.032 1.075 0.282 1.034 0.973
## status -0.017 0.039 -0.440 0.660 0.983 0.911
## ageyears 0.005 0.003 1.551 0.121 1.005 0.999
## SEXnumeric 0.023 0.032 0.703 0.482 1.023 0.960
## upper .95
## intercept 38.606
## arm 1.099
## TYPESTATUSnumeric 1.062
## HOSPITALONSETnumeric 0.889
## day 1.021
## earlyacademicyear 1.091
## white 1.115
## structuraletiology 1.065
## priorepilepsy 1.100
## status 1.061
## ageyears 1.011
## SEXnumeric 1.090
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.452 0.393 3.697 0.000 4.271 1.978
## arm -0.137 0.267 -0.515 0.607 0.872 0.517
## TYPESTATUSnumeric 0.043 0.254 0.169 0.865 1.044 0.634
## HOSPITALONSETnumeric 1.313 0.247 5.322 0.000 3.718 2.292
## day 0.379 0.252 1.504 0.133 1.461 0.892
## earlyacademicyear -0.230 0.229 -1.005 0.315 0.795 0.508
## white -0.375 0.219 -1.710 0.087 0.687 0.447
## structuraletiology 0.071 0.233 0.307 0.758 1.074 0.681
## priorepilepsy -0.294 0.282 -1.043 0.297 0.745 0.428
## status 0.190 0.315 0.605 0.545 1.210 0.653
## ageyears -0.039 0.027 -1.459 0.144 0.962 0.913
## SEXnumeric -0.221 0.251 -0.880 0.379 0.802 0.490
## upper .95
## intercept 9.220
## arm 1.471
## TYPESTATUSnumeric 1.718
## HOSPITALONSETnumeric 6.030
## day 2.393
## earlyacademicyear 1.244
## white 1.056
## structuraletiology 1.694
## priorepilepsy 1.296
## status 2.242
## ageyears 1.013
## SEXnumeric 1.311
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 120 | 148 |
## | 0.448 | 0.552 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 150
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 70 | 80 |
## | 0.467 | 0.533 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 118
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 50 | 68 |
## | 0.424 | 0.576 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore60min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore60min and pSERG$awareness
## p-value = 0.5366
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.7114192 1.9931234
## sample estimates:
## odds ratio
## 1.189223
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=60,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 1.309 -3.193 5.811 0.569
## RMST (arm=1)/(arm=0) 1.030 0.938 1.131 0.540
## RMTL (arm=1)/(arm=0) 0.928 0.634 1.360 0.702
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 48.251 3.338 14.455 0.000 41.708 54.793
## arm 1.309 2.297 0.570 0.569 -3.193 5.811
## TYPESTATUSnumeric -1.717 2.006 -0.856 0.392 -5.649 2.215
## HOSPITALONSETnumeric -13.672 2.431 -5.624 0.000 -18.437 -8.907
## day -3.259 1.967 -1.656 0.098 -7.115 0.597
## earlyacademicyear 2.282 1.935 1.179 0.238 -1.510 6.074
## white 2.436 1.983 1.228 0.219 -1.451 6.322
## structuraletiology -1.737 2.187 -0.794 0.427 -6.024 2.549
## priorepilepsy 2.523 2.014 1.253 0.210 -1.425 6.470
## status -0.953 2.351 -0.406 0.685 -5.561 3.654
## ageyears 0.333 0.197 1.694 0.090 -0.052 0.719
## SEXnumeric 2.335 1.999 1.168 0.243 -1.583 6.253
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.871 0.071 54.246 0.000 47.971 41.710
## arm 0.029 0.048 0.614 0.540 1.030 0.938
## TYPESTATUSnumeric -0.035 0.042 -0.838 0.402 0.966 0.890
## HOSPITALONSETnumeric -0.301 0.059 -5.079 0.000 0.740 0.659
## day -0.066 0.041 -1.618 0.106 0.936 0.864
## earlyacademicyear 0.046 0.040 1.134 0.257 1.047 0.967
## white 0.049 0.042 1.171 0.242 1.051 0.967
## structuraletiology -0.036 0.047 -0.752 0.452 0.965 0.880
## priorepilepsy 0.052 0.041 1.244 0.213 1.053 0.971
## status -0.018 0.049 -0.369 0.712 0.982 0.893
## ageyears 0.007 0.004 1.703 0.089 1.007 0.999
## SEXnumeric 0.047 0.042 1.137 0.255 1.049 0.966
## upper .95
## intercept 55.172
## arm 1.131
## TYPESTATUSnumeric 1.048
## HOSPITALONSETnumeric 0.831
## day 1.014
## earlyacademicyear 1.133
## white 1.141
## structuraletiology 1.059
## priorepilepsy 1.142
## status 1.081
## ageyears 1.015
## SEXnumeric 1.138
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.351 0.275 8.543 0.000 10.496 6.121
## arm -0.074 0.195 -0.382 0.702 0.928 0.634
## TYPESTATUSnumeric 0.160 0.176 0.913 0.361 1.174 0.832
## HOSPITALONSETnumeric 1.047 0.175 5.978 0.000 2.849 2.021
## day 0.313 0.180 1.736 0.083 1.367 0.960
## earlyacademicyear -0.225 0.167 -1.347 0.178 0.799 0.576
## white -0.239 0.161 -1.482 0.138 0.788 0.574
## structuraletiology 0.163 0.168 0.965 0.334 1.177 0.846
## priorepilepsy -0.241 0.196 -1.231 0.218 0.786 0.536
## status 0.120 0.216 0.554 0.579 1.127 0.738
## ageyears -0.030 0.019 -1.601 0.109 0.970 0.935
## SEXnumeric -0.236 0.177 -1.333 0.182 0.790 0.558
## upper .95
## intercept 18.000
## arm 1.360
## TYPESTATUSnumeric 1.656
## HOSPITALONSETnumeric 4.015
## day 1.947
## earlyacademicyear 1.108
## white 1.080
## structuraletiology 1.637
## priorepilepsy 1.153
## status 1.723
## ageyears 1.007
## SEXnumeric 1.117
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 268
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 187 | 81 |
## | 0.698 | 0.302 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 150
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 105 | 45 |
## | 0.700 | 0.300 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 118
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 82 | 36 |
## | 0.695 | 0.305 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG$firstASMmore120min, pSERG$awareness, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstASMmore120min and pSERG$awareness
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5845118 1.7884137
## sample estimates:
## odds ratio
## 1.024307
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG$AEDTIME.0, status=pSERG$event, arm=pSERG$awareness2017, tau=120,
covariates= pSERG[ , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.501 -10.456 11.458 0.929
## RMST (arm=1)/(arm=0) 1.014 0.870 1.182 0.858
## RMTL (arm=1)/(arm=0) 1.005 0.799 1.265 0.965
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 82.499 7.285 11.324 0.000 68.220 96.778
## arm 0.501 5.591 0.090 0.929 -10.456 11.458
## TYPESTATUSnumeric -18.280 4.432 -4.124 0.000 -26.967 -9.593
## HOSPITALONSETnumeric -31.742 5.061 -6.272 0.000 -41.660 -21.823
## day -6.318 4.542 -1.391 0.164 -15.221 2.584
## earlyacademicyear 1.704 4.501 0.379 0.705 -7.118 10.525
## white 2.677 4.607 0.581 0.561 -6.353 11.708
## structuraletiology -6.307 5.143 -1.226 0.220 -16.388 3.773
## priorepilepsy 5.650 4.779 1.182 0.237 -3.718 15.017
## status -4.869 5.591 -0.871 0.384 -15.827 6.090
## ageyears 0.811 0.441 1.841 0.066 -0.053 1.676
## SEXnumeric 4.794 4.555 1.052 0.293 -4.135 13.722
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.411 0.102 43.238 0.000 82.318 67.401
## arm 0.014 0.078 0.179 0.858 1.014 0.870
## TYPESTATUSnumeric -0.260 0.065 -3.974 0.000 0.771 0.678
## HOSPITALONSETnumeric -0.478 0.086 -5.587 0.000 0.620 0.524
## day -0.086 0.062 -1.395 0.163 0.917 0.813
## earlyacademicyear 0.018 0.062 0.291 0.771 1.018 0.902
## white 0.029 0.065 0.453 0.650 1.030 0.907
## structuraletiology -0.085 0.075 -1.132 0.258 0.918 0.792
## priorepilepsy 0.074 0.064 1.155 0.248 1.076 0.950
## status -0.057 0.078 -0.727 0.467 0.945 0.810
## ageyears 0.011 0.006 1.858 0.063 1.011 0.999
## SEXnumeric 0.065 0.063 1.037 0.300 1.067 0.944
## upper .95
## intercept 100.537
## arm 1.182
## TYPESTATUSnumeric 0.876
## HOSPITALONSETnumeric 0.733
## day 1.036
## earlyacademicyear 1.149
## white 1.168
## structuraletiology 1.065
## priorepilepsy 1.220
## status 1.102
## ageyears 1.022
## SEXnumeric 1.207
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.620 0.157 23.021 0.000 37.324 27.425
## arm 0.005 0.117 0.044 0.965 1.005 0.799
## TYPESTATUSnumeric 0.376 0.093 4.031 0.000 1.456 1.213
## HOSPITALONSETnumeric 0.626 0.101 6.166 0.000 1.870 1.533
## day 0.136 0.102 1.335 0.182 1.146 0.938
## earlyacademicyear -0.050 0.098 -0.505 0.614 0.952 0.785
## white -0.078 0.097 -0.802 0.422 0.925 0.765
## structuraletiology 0.139 0.103 1.353 0.176 1.150 0.939
## priorepilepsy -0.133 0.111 -1.198 0.231 0.875 0.704
## status 0.131 0.121 1.078 0.281 1.139 0.899
## ageyears -0.018 0.011 -1.730 0.084 0.982 0.962
## SEXnumeric -0.110 0.100 -1.098 0.272 0.896 0.736
## upper .95
## intercept 50.796
## arm 1.265
## TYPESTATUSnumeric 1.749
## HOSPITALONSETnumeric 2.281
## day 1.399
## earlyacademicyear 1.154
## white 1.119
## structuraletiology 1.407
## priorepilepsy 1.088
## status 1.445
## ageyears 1.002
## SEXnumeric 1.090
# First CI later than 60 minutes
CrossTable(pSERG$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 119
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 110 |
## | 0.076 | 0.924 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 97 |
## | 0.085 | 0.915 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$awareness2017 == 1, ]$firstCImore60min)
##
## 0 1
## 0 13
fisher.test(pSERG$firstCImore60min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore60min and pSERG$awareness2017
## p-value = 0.5947
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2320877 Inf
## sample estimates:
## odds ratio
## Inf
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness2017, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 2.813 -0.176 5.802 0.065
## RMST (arm=1)/(arm=0) 1.049 0.996 1.105 0.071
## RMTL (arm=1)/(arm=0) 0.000 0.000 0.000 0.000
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 59.323 1.597 37.146 0.000 56.193 62.453
## arm 2.813 1.525 1.845 0.065 -0.176 5.802
## TYPESTATUSnumeric 0.936 1.323 0.707 0.479 -1.657 3.529
## HOSPITALONSETnumeric -2.570 2.186 -1.176 0.240 -6.855 1.715
## day -1.132 1.070 -1.058 0.290 -3.229 0.966
## earlyacademicyear -1.182 1.575 -0.750 0.453 -4.269 1.906
## white -0.650 1.129 -0.576 0.565 -2.862 1.562
## structuraletiology 1.326 1.773 0.748 0.455 -2.150 4.801
## priorepilepsy -0.045 1.160 -0.039 0.969 -2.318 2.228
## status 2.192 1.063 2.063 0.039 0.109 4.274
## ageyears 0.042 0.122 0.342 0.732 -0.198 0.281
## SEXnumeric -0.346 1.534 -0.226 0.822 -3.352 2.660
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.083 0.028 147.262 0.000 59.303 56.167
## arm 0.048 0.027 1.803 0.071 1.049 0.996
## TYPESTATUSnumeric 0.016 0.023 0.685 0.493 1.016 0.972
## HOSPITALONSETnumeric -0.044 0.038 -1.153 0.249 0.957 0.887
## day -0.020 0.019 -1.058 0.290 0.981 0.946
## earlyacademicyear -0.020 0.027 -0.742 0.458 0.980 0.929
## white -0.011 0.019 -0.566 0.571 0.989 0.952
## structuraletiology 0.023 0.031 0.744 0.457 1.023 0.963
## priorepilepsy -0.001 0.020 -0.031 0.976 0.999 0.961
## status 0.037 0.018 2.044 0.041 1.038 1.002
## ageyears 0.001 0.002 0.345 0.730 1.001 0.997
## SEXnumeric -0.006 0.026 -0.219 0.827 0.994 0.944
## upper .95
## intercept 62.615
## arm 1.105
## TYPESTATUSnumeric 1.062
## HOSPITALONSETnumeric 1.031
## day 1.017
## earlyacademicyear 1.034
## white 1.027
## structuraletiology 1.087
## priorepilepsy 1.039
## status 1.075
## ageyears 1.005
## SEXnumeric 1.047
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept -1.684 1.618 -1.041 0.298 0.186 0.008
## arm -19.075 0.652 -29.238 0.000 0.000 0.000
## TYPESTATUSnumeric -1.422 0.924 -1.539 0.124 0.241 0.039
## HOSPITALONSETnumeric 1.725 0.920 1.875 0.061 5.614 0.925
## day 0.637 0.886 0.719 0.472 1.891 0.333
## earlyacademicyear 1.256 0.869 1.445 0.148 3.512 0.639
## white 1.034 0.805 1.284 0.199 2.812 0.580
## structuraletiology -0.747 0.985 -0.758 0.448 0.474 0.069
## priorepilepsy -0.134 0.738 -0.182 0.855 0.874 0.206
## status -18.576 0.746 -24.896 0.000 0.000 0.000
## ageyears 0.008 0.059 0.143 0.887 1.009 0.898
## SEXnumeric 0.538 0.916 0.587 0.557 1.712 0.284
## upper .95
## intercept 4.423
## arm 0.000
## TYPESTATUSnumeric 1.476
## HOSPITALONSETnumeric 34.083
## day 10.746
## earlyacademicyear 19.289
## white 13.621
## structuraletiology 3.266
## priorepilepsy 3.713
## status 0.000
## ageyears 1.133
## SEXnumeric 10.306
# First CI later than 120 minutes
CrossTable(pSERG$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 119
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 91 |
## | 0.235 | 0.765 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 25 | 81 |
## | 0.236 | 0.764 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$awareness2017 == 1, ]$firstCImore120min)
##
## 0 1
## 3 10
fisher.test(pSERG$firstCImore120min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore120min and pSERG$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2389733 6.2641599
## sample estimates:
## odds ratio
## 1.028581
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness2017, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 8.311 -1.259 17.880 0.089
## RMST (arm=1)/(arm=0) 1.076 0.988 1.173 0.094
## RMTL (arm=1)/(arm=0) 0.267 0.041 1.740 0.168
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 110.929 6.574 16.874 0.000 98.044 123.814
## arm 8.311 4.882 1.702 0.089 -1.259 17.880
## TYPESTATUSnumeric 2.249 4.610 0.488 0.626 -6.786 11.284
## HOSPITALONSETnumeric 0.308 5.510 0.056 0.955 -10.492 11.108
## day -5.780 4.012 -1.441 0.150 -13.643 2.083
## earlyacademicyear -6.705 4.825 -1.390 0.165 -16.162 2.752
## white 4.376 4.376 1.000 0.317 -4.201 12.953
## structuraletiology 2.226 5.600 0.398 0.691 -8.749 13.202
## priorepilepsy -2.544 4.777 -0.533 0.594 -11.907 6.819
## status 10.937 4.076 2.683 0.007 2.949 18.926
## ageyears 0.204 0.389 0.525 0.600 -0.558 0.967
## SEXnumeric -2.537 4.612 -0.550 0.582 -11.576 6.501
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.707 0.059 79.134 0.000 110.748 98.561
## arm 0.073 0.044 1.675 0.094 1.076 0.988
## TYPESTATUSnumeric 0.020 0.042 0.482 0.630 1.020 0.940
## HOSPITALONSETnumeric 0.003 0.050 0.059 0.953 1.003 0.909
## day -0.053 0.037 -1.440 0.150 0.948 0.882
## earlyacademicyear -0.061 0.044 -1.374 0.169 0.941 0.863
## white 0.040 0.040 0.996 0.319 1.041 0.962
## structuraletiology 0.020 0.051 0.391 0.696 1.020 0.923
## priorepilepsy -0.023 0.044 -0.530 0.596 0.977 0.897
## status 0.098 0.037 2.634 0.008 1.103 1.025
## ageyears 0.002 0.004 0.535 0.593 1.002 0.995
## SEXnumeric -0.023 0.042 -0.544 0.586 0.977 0.900
## upper .95
## intercept 124.443
## arm 1.173
## TYPESTATUSnumeric 1.108
## HOSPITALONSETnumeric 1.106
## day 1.019
## earlyacademicyear 1.026
## white 1.126
## structuraletiology 1.128
## priorepilepsy 1.064
## status 1.186
## ageyears 1.009
## SEXnumeric 1.061
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.963 0.792 2.479 0.013 7.120 1.508
## arm -1.319 0.955 -1.380 0.168 0.267 0.041
## TYPESTATUSnumeric -0.265 0.511 -0.519 0.604 0.767 0.282
## HOSPITALONSETnumeric -0.030 0.599 -0.050 0.960 0.970 0.300
## day 0.549 0.440 1.247 0.212 1.731 0.731
## earlyacademicyear 0.767 0.548 1.398 0.162 2.152 0.735
## white -0.397 0.446 -0.890 0.373 0.672 0.280
## structuraletiology -0.248 0.548 -0.452 0.651 0.781 0.267
## priorepilepsy 0.251 0.477 0.526 0.599 1.285 0.505
## status -1.672 0.745 -2.246 0.025 0.188 0.044
## ageyears -0.016 0.040 -0.407 0.684 0.984 0.909
## SEXnumeric 0.299 0.465 0.644 0.519 1.349 0.543
## upper .95
## intercept 33.615
## arm 1.740
## TYPESTATUSnumeric 2.087
## HOSPITALONSETnumeric 3.142
## day 4.100
## earlyacademicyear 6.305
## white 1.612
## structuraletiology 2.283
## priorepilepsy 3.270
## status 0.808
## ageyears 1.065
## SEXnumeric 3.355
# First CI later than 240 minutes
CrossTable(pSERG$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 119
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 68 | 51 |
## | 0.571 | 0.429 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$awareness2017 == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 106
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 62 | 44 |
## | 0.585 | 0.415 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$awareness2017 == 1, ]$firstCImore240min)
##
## 0 1
## 6 7
fisher.test(pSERG$firstCImore240min, pSERG$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG$firstCImore240min and pSERG$awareness2017
## p-value = 0.5541
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4374766 6.3369036
## sample estimates:
## odds ratio
## 1.63691
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0), ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0), ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0), ]$awareness2017, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) , c("TYPESTATUSnumeric", "HOSPITALONSETnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 22.471 -13.432 58.373 0.220
## RMST (arm=1)/(arm=0) 1.133 0.937 1.370 0.199
## RMTL (arm=1)/(arm=0) 0.676 0.318 1.435 0.308
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 187.100 20.827 8.983 0.000 146.279 227.921
## arm 22.471 18.318 1.227 0.220 -13.432 58.373
## TYPESTATUSnumeric -17.865 13.527 -1.321 0.187 -44.377 8.648
## HOSPITALONSETnumeric 1.619 14.172 0.114 0.909 -26.158 29.395
## day -9.967 12.636 -0.789 0.430 -34.733 14.799
## earlyacademicyear -20.139 13.243 -1.521 0.128 -46.095 5.818
## white 13.370 13.087 1.022 0.307 -12.280 39.021
## structuraletiology 3.765 15.631 0.241 0.810 -26.871 34.402
## priorepilepsy 3.519 14.531 0.242 0.809 -24.960 31.999
## status 12.451 14.329 0.869 0.385 -15.632 40.535
## ageyears 0.292 1.190 0.245 0.806 -2.040 2.624
## SEXnumeric -12.543 12.496 -1.004 0.316 -37.035 11.950
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.228 0.115 45.471 0.000 186.341 148.748
## arm 0.125 0.097 1.284 0.199 1.133 0.937
## TYPESTATUSnumeric -0.106 0.081 -1.311 0.190 0.900 0.768
## HOSPITALONSETnumeric 0.006 0.079 0.081 0.936 1.006 0.861
## day -0.054 0.071 -0.767 0.443 0.947 0.824
## earlyacademicyear -0.113 0.075 -1.511 0.131 0.893 0.772
## white 0.076 0.075 1.002 0.317 1.079 0.930
## structuraletiology 0.021 0.088 0.234 0.815 1.021 0.859
## priorepilepsy 0.020 0.080 0.245 0.807 1.020 0.871
## status 0.074 0.080 0.922 0.356 1.076 0.921
## ageyears 0.002 0.007 0.266 0.790 1.002 0.989
## SEXnumeric -0.071 0.071 -1.005 0.315 0.931 0.811
## upper .95
## intercept 233.436
## arm 1.370
## TYPESTATUSnumeric 1.054
## HOSPITALONSETnumeric 1.176
## day 1.088
## earlyacademicyear 1.034
## white 1.251
## structuraletiology 1.212
## priorepilepsy 1.193
## status 1.258
## ageyears 1.015
## SEXnumeric 1.070
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.934 0.364 10.819 0.000 51.115 25.062
## arm -0.392 0.384 -1.020 0.308 0.676 0.318
## TYPESTATUSnumeric 0.256 0.196 1.310 0.190 1.292 0.881
## HOSPITALONSETnumeric -0.050 0.235 -0.213 0.832 0.951 0.601
## day 0.180 0.213 0.845 0.398 1.198 0.788
## earlyacademicyear 0.335 0.223 1.504 0.133 1.397 0.903
## white -0.215 0.202 -1.067 0.286 0.807 0.543
## structuraletiology -0.069 0.255 -0.271 0.786 0.933 0.566
## priorepilepsy -0.059 0.247 -0.237 0.813 0.943 0.581
## status -0.176 0.248 -0.709 0.478 0.839 0.516
## ageyears -0.004 0.020 -0.204 0.838 0.996 0.957
## SEXnumeric 0.199 0.202 0.986 0.324 1.220 0.822
## upper .95
## intercept 104.251
## arm 1.435
## TYPESTATUSnumeric 1.896
## HOSPITALONSETnumeric 1.507
## day 1.820
## earlyacademicyear 2.162
## white 1.197
## structuraletiology 1.539
## priorepilepsy 1.532
## status 1.363
## ageyears 1.037
## SEXnumeric 1.811
## OUT OF THE HOSPITAL
# At least one benzodiazepine before hospital arrival
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 134
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 51 | 83 |
## | 0.381 | 0.619 |
## |-----------|-----------|
##
##
##
##
# At least one benzodiazepine before hospital arrival depending on awareness
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 119
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 49 | 70 |
## | 0.412 | 0.588 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 15
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 2 | 13 |
## | 0.133 | 0.867 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017)
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$AEDbeforehospital and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.04742
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.9547557 42.8994588
## sample estimates:
## odds ratio
## 4.508736
# Logistic regression adjusting for potential confounders
logistic_out_of_hospital_BZD <- glm(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDbeforehospital ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX, family="binomial")
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD), confint(logistic_out_of_hospital_BZD, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD))[ , 4])
## Waiting for profiling to be done...
## Odds ratio
## (Intercept) 3.1529206
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 6.7018034
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.3606124
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.9623272
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.1789207
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.6933350
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8537765
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.6867202
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 5.8802132
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0454124
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7127887
## 2.5 %
## (Intercept) 0.8319403
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.4978445
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.1422851
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.4225197
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.5372640
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.2984167
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.3431411
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.3019876
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.7584253
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9689084
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.3213066
## 97.5 %
## (Intercept) 12.9783426
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 48.4799732
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.8596642
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 2.1887620
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.6063614
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.5731914
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 2.1518086
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.5309597
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 27.1792974
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.1323667
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.5602041
## p-value
## (Intercept) 0.09879390
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.02518522
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.02512855
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.92668859
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.68146248
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.38513324
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.73392250
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.36180994
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.00891462
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.25999331
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.39879097
# At least one benzodiazepine before hospital arrival among those with prior epilepsy
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 74
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 46 |
## | 0.378 | 0.622 |
## |-----------|-----------|
##
##
##
##
# At least one benzodiazepine before hospital arrival among those with prior epilepsy depending on awareness
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness2017 == 0), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 63
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 27 | 36 |
## | 0.429 | 0.571 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1 & pSERG$awareness2017 == 1), ]$AEDbeforehospital)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 11
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 1 | 10 |
## | 0.091 | 0.909 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital, pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$awareness2017)
##
## Fisher's Exact Test for Count Data
##
## data:
## p-value = 0.04365
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.9420621 336.7607655
## sample estimates:
## odds ratio
## 7.347579
# Logistic regression adjusting for potential confounders among those with prior epilepsy
logistic_out_of_hospital_BZD_prior_epilepsy <- glm(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$AEDbeforehospital ~ pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$day + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$white +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$structuraletiology +
pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$status + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no" & pSERG$priorepilepsy==1, ]$SEX, family="binomial")
cbind(exp(cbind("Odds ratio" = coef(logistic_out_of_hospital_BZD_prior_epilepsy), confint(logistic_out_of_hospital_BZD_prior_epilepsy, level = 0.95))), "p-value" = coef(summary(logistic_out_of_hospital_BZD_prior_epilepsy))[ , 4])
## Waiting for profiling to be done...
## Odds ratio
## (Intercept) 1.5943765
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness2017 15.4364889
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.3257898
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 1.3960338
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.5267898
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.4876958
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 1.4629884
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 8.9357697
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 1.1321480
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.7770633
## 2.5 %
## (Intercept) 0.20388938
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness2017 1.82958783
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.07934376
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 0.40448177
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.14344334
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.13080539
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 0.37904198
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 1.74042691
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 0.99884865
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.22334930
## 97.5 %
## (Intercept) 13.587188
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness2017 358.118449
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 1.192805
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 4.944381
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 1.803797
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 1.692243
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 6.043288
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 73.479634
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 1.307473
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 2.623113
## p-value
## (Intercept) 0.65777764
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$awareness2017 0.02881430
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$TYPESTATUSintermittent 0.10013803
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$day 0.59635903
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$earlyacademicyear 0.31486118
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$white 0.26489324
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$structuraletiology 0.58565603
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$status 0.01759709
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$ageyears 0.06594680
## pSERG[pSERG$HOSPITALONSET == "no" & pSERG$priorepilepsy == 1, ]$SEXmale 0.68396910
# Patients in each category
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 154 | 30 |
## | 0.837 | 0.163 |
## |-----------|-----------|
##
##
##
##
# Time to first BZD
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 7.00 21.50 68.46 55.00 1264.00
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0)
## [1] 154.9925
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 184.0 184.0 21.5 20.0 30.0
# Figure time to first BZD
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 7.25 21.50 71.40 58.75 1264.00
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 6.25 25.00 53.40 44.25 330.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 104 52.8 56.6
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 80 43.7 39.9
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 0.253 0.96
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 0.360 0.96
##
## Chisq= 1 on 1 degrees of freedom, p= 0.3
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.3272417
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 184, number of events= 184
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.037281
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.430975
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.026219
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.121744
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.150304
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.133821
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.059880
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.542730
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.005961
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.086885
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.963405
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.649875
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.026566
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.129465
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.162187
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.143188
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.061709
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.720698
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.005978
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.090771
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.212936
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.168741
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.157271
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.155257
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.161067
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.187873
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.159752
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.214843
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.016146
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.153512
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.175
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -2.554
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.167
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.784
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.933
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.712
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.375
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.526
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.369
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.566
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.8610
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.0106 *
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8676
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.4330
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.3507
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.4763
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.7078
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.0115 *
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.7120
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.5714
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.9634
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6499
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.0266
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.1295
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.1622
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.1432
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0617
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.7207
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0060
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.0908
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.0380
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.5388
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.9741
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8854
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.8604
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8747
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.9419
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.5812
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9941
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.9168
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.6347
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.4669
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.7543
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.8331
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.8476
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.7910
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.7763
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.1294
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9746
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8074
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.4624
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.9046
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.3972
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.5312
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.5936
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.6521
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.4521
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.6217
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0383
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.4737
##
## Concordance= 0.59 (se = 0.027 )
## Rsquare= 0.088 (max possible= 1 )
## Likelihood ratio test= 16.9 on 10 df, p=0.08
## Wald test = 17.86 on 10 df, p=0.06
## Score (logrank) test = 18.41 on 10 df, p=0.05
# Time to first non-BZD AED
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 50.0 86.0 184.3 175.2 1800.0
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0)
## [1] 269.1477
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 184 184 86 69 115
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 47.25 88.50 187.08 169.00 1800.00
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 58.25 77.00 170.23 206.75 720.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 104 53.8 51.5
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 80 39.4 41.6
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 0.0987 0.333
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 0.1221 0.333
##
## Chisq= 0.3 on 1 degrees of freedom, p= 0.6
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.5638338
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 184, number of events= 184
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.12754
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -0.70081
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.11229
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.01565
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.06712
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology -0.04680
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy -0.07505
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.06453
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -0.02225
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.13378
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.88026
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.49618
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.11884
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.01577
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.93508
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.95428
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.92770
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.06665
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.97800
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.14315
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.21073
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.16909
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.16004
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.15601
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.15732
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.18431
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.16528
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.21525
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.01570
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.16036
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.605
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent -4.144
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.702
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.100
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.427
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology -0.254
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy -0.454
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.300
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears -1.417
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.834
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.545
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 3.41e-05 ***
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.483
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.920
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.670
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.800
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.650
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.764
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.156
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.8803
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.4962
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.1188
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.0158
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.9351
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.9543
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.9277
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.0667
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9780
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.1431
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.1360
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 2.0154
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8938
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.9845
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.0694
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.0479
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0779
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.9375
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0225
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8748
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.5824
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.3562
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.8176
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.7482
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.6870
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.6650
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.6710
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.6995
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9484
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.8348
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.3304
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.6912
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.5310
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.3791
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.2728
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.3695
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.2826
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.6265
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0086
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.5653
##
## Concordance= 0.609 (se = 0.025 )
## Rsquare= 0.118 (max possible= 1 )
## Likelihood ratio test= 23.21 on 10 df, p=0.01
## Wald test = 23.72 on 10 df, p=0.008
## Score (logrank) test = 24.58 on 10 df, p=0.006
# Time to first CI
summary(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20.0 124.2 193.0 504.4 657.0 4320.0 100
sd(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## 1)
##
## 100 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 84 84 193 155 330
# Figure time to first CI
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20.0 125.0 180.0 483.4 626.0 4320.0 77
summary(pSERG[which(pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 75 330 600 735 1188 1435 23
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness, rho = 1)
##
## n=84, 100 observations deleted due to missingness.
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 44 22.3 22.3
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 40 20.3 20.3
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=0 5.71e-05 0.00018
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness=1 6.29e-05 0.00018
##
## Chisq= 0 on 1 degrees of freedom, p= 1
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.9893053
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="no", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="no", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="no", ]$day + pSERG[pSERG$HOSPITALONSET=="no", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="no", ]$white +
pSERG[pSERG$HOSPITALONSET=="no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="no", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="no", ]$status + pSERG[pSERG$HOSPITALONSET=="no", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="no", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "no", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "no",
## ]$day + pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "no", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "no", ]$status + pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears +
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEX)
##
## n= 84, number of events= 84
## (100 observations deleted due to missingness)
##
## coef
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.2799580
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.0045694
## pSERG[pSERG$HOSPITALONSET == "no", ]$day -0.0854560
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.6797016
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -0.5447898
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.4083752
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.0584413
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.2000818
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.0003276
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.2266975
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.7558155
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.0045798
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.9180935
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.9732889
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.5799637
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.5043715
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0601828
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.2215027
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0003277
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.2544503
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.4240482
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.2697624
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.2363041
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.2654572
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.2797099
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.3006407
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.2849113
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.3202091
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.0249597
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.2565803
## z
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 -0.660
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.017
## pSERG[pSERG$HOSPITALONSET == "no", ]$day -0.362
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 2.560
## pSERG[pSERG$HOSPITALONSET == "no", ]$white -1.948
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.358
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.205
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.625
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.013
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.884
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.5091
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.9865
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.7176
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.0105 *
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.0515 .
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.1744
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.8375
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.5321
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9895
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.3769
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.7558
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.0046
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.9181
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.9733
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.5800
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 1.5044
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.0602
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 1.2215
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.0003
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 1.2545
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.3231
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.9954
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.0892
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 0.5068
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.7242
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.6647
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.9432
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.8187
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9997
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7972
## lower .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 0.3292
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 0.5921
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 0.5778
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 1.1728
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 0.3352
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 0.8345
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 0.6065
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 0.6521
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 0.9526
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 0.7587
## upper .95
## pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017 1.735
## pSERG[pSERG$HOSPITALONSET == "no", ]$TYPESTATUSintermittent 1.705
## pSERG[pSERG$HOSPITALONSET == "no", ]$day 1.459
## pSERG[pSERG$HOSPITALONSET == "no", ]$earlyacademicyear 3.320
## pSERG[pSERG$HOSPITALONSET == "no", ]$white 1.003
## pSERG[pSERG$HOSPITALONSET == "no", ]$structuraletiology 2.712
## pSERG[pSERG$HOSPITALONSET == "no", ]$priorepilepsy 1.853
## pSERG[pSERG$HOSPITALONSET == "no", ]$status 2.288
## pSERG[pSERG$HOSPITALONSET == "no", ]$ageyears 1.050
## pSERG[pSERG$HOSPITALONSET == "no", ]$SEXmale 2.074
##
## Concordance= 0.611 (se = 0.037 )
## Rsquare= 0.139 (max possible= 0.999 )
## Likelihood ratio test= 12.56 on 10 df, p=0.2
## Wald test = 12.27 on 10 df, p=0.3
## Score (logrank) test = 12.52 on 10 df, p=0.3
#### Recommendations and outliers out of the hospital
# First BZD later than 20 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 92 | 92 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 154
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 77 | 77 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 30
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 15 | 15 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore20min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore20min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4229168 2.3645315
## sample estimates:
## odds ratio
## 1
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=20,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.353 -2.383 3.090 0.800
## RMST (arm=1)/(arm=0) 1.022 0.847 1.234 0.819
## RMTL (arm=1)/(arm=0) 0.922 0.550 1.546 0.759
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 18.083 1.673 10.807 0.000 14.803 21.363
## arm 0.353 1.396 0.253 0.800 -2.383 3.090
## TYPESTATUSnumeric 0.012 1.012 0.012 0.991 -1.971 1.995
## day -0.647 0.989 -0.654 0.513 -2.585 1.291
## earlyacademicyear -0.030 0.969 -0.031 0.975 -1.930 1.869
## white -1.063 0.983 -1.081 0.280 -2.990 0.864
## structuraletiology -0.922 1.171 -0.787 0.431 -3.218 1.374
## priorepilepsy -1.912 0.988 -1.935 0.053 -3.850 0.025
## status -4.822 1.490 -3.236 0.001 -7.742 -1.902
## ageyears -0.077 0.101 -0.760 0.447 -0.275 0.121
## SEXnumeric 0.203 0.988 0.206 0.837 -1.733 2.139
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.913 0.110 26.531 0.000 18.404 14.841
## arm 0.022 0.096 0.229 0.819 1.022 0.847
## TYPESTATUSnumeric 0.000 0.068 -0.004 0.997 1.000 0.874
## day -0.045 0.067 -0.672 0.502 0.956 0.837
## earlyacademicyear -0.002 0.065 -0.027 0.979 0.998 0.878
## white -0.072 0.066 -1.088 0.276 0.931 0.818
## structuraletiology -0.062 0.080 -0.776 0.437 0.940 0.803
## priorepilepsy -0.127 0.068 -1.877 0.061 0.880 0.771
## status -0.377 0.134 -2.812 0.005 0.686 0.528
## ageyears -0.005 0.007 -0.782 0.434 0.995 0.981
## SEXnumeric 0.015 0.068 0.214 0.831 1.015 0.888
## upper .95
## intercept 22.823
## arm 1.234
## TYPESTATUSnumeric 1.143
## day 1.091
## earlyacademicyear 1.135
## white 1.059
## structuraletiology 1.100
## priorepilepsy 1.006
## status 0.892
## ageyears 1.008
## SEXnumeric 1.159
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 0.971 0.372 2.608 0.009 2.641 1.273
## arm -0.081 0.264 -0.307 0.759 0.922 0.550
## TYPESTATUSnumeric -0.010 0.199 -0.051 0.959 0.990 0.670
## day 0.114 0.189 0.603 0.546 1.121 0.774
## earlyacademicyear 0.008 0.192 0.041 0.967 1.008 0.692
## white 0.204 0.198 1.030 0.303 1.226 0.832
## structuraletiology 0.178 0.221 0.805 0.421 1.195 0.775
## priorepilepsy 0.400 0.204 1.964 0.050 1.492 1.001
## status 0.718 0.200 3.598 0.000 2.050 1.386
## ageyears 0.013 0.019 0.670 0.503 1.013 0.976
## SEXnumeric -0.034 0.190 -0.178 0.859 0.967 0.666
## upper .95
## intercept 5.477
## arm 1.546
## TYPESTATUSnumeric 1.461
## day 1.622
## earlyacademicyear 1.468
## white 1.807
## structuraletiology 1.842
## priorepilepsy 2.224
## status 3.031
## ageyears 1.051
## SEXnumeric 1.403
# First BZD later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 123 | 61 |
## | 0.668 | 0.332 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 154
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 102 | 52 |
## | 0.662 | 0.338 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 30
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 21 | 9 |
## | 0.700 | 0.300 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore40min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore40min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.8328
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3158394 2.0838829
## sample estimates:
## odds ratio
## 0.841457
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.476 -5.567 6.519 0.877
## RMST (arm=1)/(arm=0) 1.022 0.783 1.333 0.873
## RMTL (arm=1)/(arm=0) 0.976 0.685 1.389 0.891
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 28.277 3.457 8.180 0.000 21.502 35.053
## arm 0.476 3.083 0.154 0.877 -5.567 6.519
## TYPESTATUSnumeric -2.929 2.160 -1.356 0.175 -7.163 1.305
## day -0.751 2.159 -0.348 0.728 -4.983 3.481
## earlyacademicyear -0.640 2.090 -0.306 0.759 -4.736 3.456
## white -0.691 2.142 -0.323 0.747 -4.889 3.507
## structuraletiology 0.266 2.608 0.102 0.919 -4.845 5.377
## priorepilepsy -0.971 2.185 -0.444 0.657 -5.255 3.312
## status -8.705 3.037 -2.867 0.004 -14.657 -2.754
## ageyears -0.176 0.216 -0.815 0.415 -0.598 0.247
## SEXnumeric -0.150 2.181 -0.069 0.945 -4.424 4.124
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.358 0.145 23.216 0.000 28.742 21.646
## arm 0.022 0.136 0.159 0.873 1.022 0.783
## TYPESTATUSnumeric -0.129 0.096 -1.344 0.179 0.879 0.727
## day -0.036 0.094 -0.382 0.702 0.965 0.803
## earlyacademicyear -0.026 0.090 -0.284 0.776 0.975 0.817
## white -0.028 0.093 -0.307 0.759 0.972 0.810
## structuraletiology 0.013 0.109 0.116 0.908 1.013 0.817
## priorepilepsy -0.045 0.093 -0.478 0.633 0.956 0.796
## status -0.447 0.181 -2.467 0.014 0.639 0.448
## ageyears -0.008 0.010 -0.796 0.426 0.992 0.974
## SEXnumeric -0.004 0.095 -0.037 0.970 0.996 0.827
## upper .95
## intercept 38.163
## arm 1.333
## TYPESTATUSnumeric 1.061
## day 1.159
## earlyacademicyear 1.163
## white 1.166
## structuraletiology 1.255
## priorepilepsy 1.148
## status 0.912
## ageyears 1.011
## SEXnumeric 1.201
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.508 0.219 11.444 0.000 12.282 7.993
## arm -0.025 0.180 -0.137 0.891 0.976 0.685
## TYPESTATUSnumeric 0.171 0.127 1.348 0.178 1.186 0.925
## day 0.039 0.129 0.300 0.764 1.039 0.808
## earlyacademicyear 0.042 0.125 0.335 0.738 1.043 0.816
## white 0.044 0.127 0.343 0.732 1.045 0.814
## structuraletiology -0.014 0.163 -0.088 0.930 0.986 0.716
## priorepilepsy 0.053 0.134 0.399 0.690 1.055 0.812
## status 0.441 0.145 3.045 0.002 1.554 1.170
## ageyears 0.010 0.013 0.833 0.405 1.011 0.986
## SEXnumeric 0.014 0.130 0.109 0.913 1.014 0.786
## upper .95
## intercept 18.873
## arm 1.389
## TYPESTATUSnumeric 1.521
## day 1.338
## earlyacademicyear 1.333
## white 1.341
## structuraletiology 1.357
## priorepilepsy 1.371
## status 2.063
## ageyears 1.036
## SEXnumeric 1.308
# First BZD later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 144 | 40 |
## | 0.783 | 0.217 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 154
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 119 | 35 |
## | 0.773 | 0.227 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 30
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 25 | 5 |
## | 0.833 | 0.167 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstBZDmore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstBZDmore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.6293
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.189624 1.998742
## sample estimates:
## odds ratio
## 0.6813211
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.619 -9.236 7.998 0.888
## RMST (arm=1)/(arm=0) 0.982 0.718 1.342 0.909
## RMTL (arm=1)/(arm=0) 1.024 0.784 1.337 0.865
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 36.598 5.145 7.113 0.000 26.514 46.682
## arm -0.619 4.396 -0.141 0.888 -9.236 7.998
## TYPESTATUSnumeric -6.005 3.173 -1.893 0.058 -12.223 0.214
## day -1.095 3.185 -0.344 0.731 -7.337 5.147
## earlyacademicyear -1.459 3.080 -0.474 0.636 -7.496 4.579
## white -1.223 3.197 -0.383 0.702 -7.488 5.042
## structuraletiology 0.859 3.849 0.223 0.823 -6.685 8.403
## priorepilepsy 1.468 3.273 0.449 0.654 -4.947 7.883
## status -13.050 4.058 -3.216 0.001 -21.004 -5.096
## ageyears -0.296 0.312 -0.951 0.341 -0.907 0.314
## SEXnumeric -0.979 3.233 -0.303 0.762 -7.316 5.357
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.620 0.172 21.076 0.000 37.334 26.663
## arm -0.018 0.159 -0.114 0.909 0.982 0.718
## TYPESTATUSnumeric -0.214 0.116 -1.841 0.066 0.807 0.643
## day -0.042 0.111 -0.375 0.707 0.959 0.772
## earlyacademicyear -0.046 0.107 -0.427 0.669 0.955 0.775
## white -0.040 0.112 -0.356 0.722 0.961 0.772
## structuraletiology 0.030 0.128 0.235 0.814 1.030 0.802
## priorepilepsy 0.044 0.111 0.399 0.690 1.045 0.841
## status -0.553 0.204 -2.706 0.007 0.575 0.385
## ageyears -0.010 0.011 -0.920 0.358 0.990 0.968
## SEXnumeric -0.028 0.113 -0.251 0.802 0.972 0.780
## upper .95
## intercept 52.276
## arm 1.342
## TYPESTATUSnumeric 1.014
## day 1.192
## earlyacademicyear 1.177
## white 1.196
## structuraletiology 1.323
## priorepilepsy 1.298
## status 0.859
## ageyears 1.012
## SEXnumeric 1.212
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.179 0.175 18.171 0.000 24.015 17.044
## arm 0.023 0.136 0.171 0.865 1.024 0.784
## TYPESTATUSnumeric 0.190 0.101 1.892 0.058 1.210 0.993
## day 0.033 0.103 0.317 0.751 1.033 0.844
## earlyacademicyear 0.051 0.100 0.515 0.607 1.053 0.866
## white 0.042 0.103 0.406 0.685 1.043 0.852
## structuraletiology -0.028 0.130 -0.214 0.830 0.972 0.753
## priorepilepsy -0.053 0.108 -0.487 0.627 0.949 0.767
## status 0.372 0.112 3.326 0.001 1.451 1.165
## ageyears 0.010 0.010 0.970 0.332 1.010 0.990
## SEXnumeric 0.036 0.105 0.344 0.731 1.037 0.844
## upper .95
## intercept 33.836
## arm 1.337
## TYPESTATUSnumeric 1.473
## day 1.264
## earlyacademicyear 1.280
## white 1.276
## structuraletiology 1.256
## priorepilepsy 1.173
## status 1.806
## ageyears 1.029
## SEXnumeric 1.273
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 33 | 151 |
## | 0.179 | 0.821 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 154
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 30 | 124 |
## | 0.195 | 0.805 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 30
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 27 |
## | 0.100 | 0.900 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore40min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore40min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.3004
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6040606 11.9112959
## sample estimates:
## odds ratio
## 2.169754
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.993 -1.627 3.614 0.458
## RMST (arm=1)/(arm=0) 1.027 0.958 1.100 0.451
## RMTL (arm=1)/(arm=0) 0.678 0.180 2.551 0.565
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 34.263 2.065 16.594 0.000 30.216 38.309
## arm 0.993 1.337 0.743 0.458 -1.627 3.614
## TYPESTATUSnumeric -0.877 1.023 -0.857 0.391 -2.882 1.128
## day -0.452 1.135 -0.398 0.691 -2.677 1.773
## earlyacademicyear 1.599 1.056 1.513 0.130 -0.472 3.669
## white 0.741 1.109 0.668 0.504 -1.433 2.915
## structuraletiology 0.739 1.231 0.600 0.548 -1.674 3.153
## priorepilepsy 1.580 1.167 1.354 0.176 -0.707 3.866
## status 0.824 1.153 0.715 0.475 -1.436 3.085
## ageyears 0.075 0.112 0.672 0.502 -0.144 0.295
## SEXnumeric 1.308 1.109 1.179 0.238 -0.866 3.482
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.536 0.057 61.939 0.000 34.335 30.700
## arm 0.027 0.035 0.753 0.451 1.027 0.958
## TYPESTATUSnumeric -0.023 0.028 -0.843 0.399 0.977 0.926
## day -0.012 0.030 -0.400 0.689 0.988 0.931
## earlyacademicyear 0.043 0.029 1.496 0.135 1.044 0.987
## white 0.020 0.030 0.673 0.501 1.020 0.962
## structuraletiology 0.020 0.033 0.604 0.546 1.020 0.956
## priorepilepsy 0.042 0.031 1.352 0.176 1.043 0.981
## status 0.022 0.030 0.709 0.478 1.022 0.963
## ageyears 0.002 0.003 0.687 0.492 1.002 0.996
## SEXnumeric 0.035 0.030 1.171 0.242 1.036 0.977
## upper .95
## intercept 38.400
## arm 1.100
## TYPESTATUSnumeric 1.031
## day 1.049
## earlyacademicyear 1.104
## white 1.082
## structuraletiology 1.088
## priorepilepsy 1.110
## status 1.085
## ageyears 1.008
## SEXnumeric 1.098
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 1.902 0.560 3.398 0.001 6.696 2.236
## arm -0.389 0.676 -0.575 0.565 0.678 0.180
## TYPESTATUSnumeric 0.374 0.378 0.990 0.322 1.453 0.693
## day 0.164 0.436 0.375 0.707 1.178 0.501
## earlyacademicyear -0.661 0.425 -1.556 0.120 0.516 0.225
## white -0.218 0.401 -0.545 0.586 0.804 0.366
## structuraletiology -0.277 0.483 -0.574 0.566 0.758 0.294
## priorepilepsy -0.605 0.489 -1.237 0.216 0.546 0.210
## status -0.404 0.577 -0.700 0.484 0.668 0.216
## ageyears -0.022 0.049 -0.447 0.655 0.978 0.889
## SEXnumeric -0.503 0.410 -1.226 0.220 0.605 0.271
## upper .95
## intercept 20.053
## arm 2.551
## TYPESTATUSnumeric 3.047
## day 2.770
## earlyacademicyear 1.187
## white 1.764
## structuraletiology 1.953
## priorepilepsy 1.424
## status 2.069
## ageyears 1.077
## SEXnumeric 1.351
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 64 | 120 |
## | 0.348 | 0.652 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 154
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 55 | 99 |
## | 0.357 | 0.643 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 30
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 21 |
## | 0.300 | 0.700 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.6763
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5238944 3.4427485
## sample estimates:
## odds ratio
## 1.294519
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 2.761 -2.208 7.730 0.276
## RMST (arm=1)/(arm=0) 1.054 0.961 1.156 0.266
## RMTL (arm=1)/(arm=0) 0.680 0.288 1.603 0.378
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 46.149 3.713 12.429 0.000 38.872 53.427
## arm 2.761 2.535 1.089 0.276 -2.208 7.730
## TYPESTATUSnumeric -2.503 1.979 -1.264 0.206 -6.381 1.376
## day -1.347 2.105 -0.640 0.522 -5.473 2.778
## earlyacademicyear 3.248 2.023 1.605 0.108 -0.718 7.214
## white 1.259 2.132 0.591 0.555 -2.919 5.437
## structuraletiology 0.685 2.419 0.283 0.777 -4.057 5.427
## priorepilepsy 4.062 2.150 1.890 0.059 -0.151 8.276
## status 1.720 2.212 0.777 0.437 -2.616 6.055
## ageyears 0.194 0.211 0.920 0.358 -0.219 0.607
## SEXnumeric 2.746 2.076 1.323 0.186 -1.322 6.815
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.836 0.074 51.585 0.000 46.324 40.041
## arm 0.052 0.047 1.113 0.266 1.054 0.961
## TYPESTATUSnumeric -0.047 0.039 -1.231 0.218 0.954 0.884
## day -0.026 0.040 -0.643 0.520 0.974 0.901
## earlyacademicyear 0.062 0.039 1.586 0.113 1.064 0.986
## white 0.025 0.041 0.598 0.550 1.025 0.945
## structuraletiology 0.013 0.046 0.289 0.773 1.014 0.925
## priorepilepsy 0.078 0.041 1.881 0.060 1.081 0.997
## status 0.032 0.041 0.769 0.442 1.032 0.952
## ageyears 0.004 0.004 0.950 0.342 1.004 0.996
## SEXnumeric 0.053 0.040 1.310 0.190 1.054 0.974
## upper .95
## intercept 53.591
## arm 1.156
## TYPESTATUSnumeric 1.028
## day 1.054
## earlyacademicyear 1.148
## white 1.111
## structuraletiology 1.110
## priorepilepsy 1.173
## status 1.119
## ageyears 1.012
## SEXnumeric 1.141
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.699 0.386 6.988 0.000 14.863 6.972
## arm -0.386 0.438 -0.882 0.378 0.680 0.288
## TYPESTATUSnumeric 0.346 0.246 1.408 0.159 1.413 0.873
## day 0.173 0.282 0.612 0.541 1.189 0.684
## earlyacademicyear -0.452 0.281 -1.610 0.107 0.636 0.367
## white -0.136 0.265 -0.511 0.609 0.873 0.519
## structuraletiology -0.085 0.310 -0.275 0.784 0.919 0.501
## priorepilepsy -0.541 0.307 -1.764 0.078 0.582 0.319
## status -0.272 0.367 -0.741 0.458 0.762 0.371
## ageyears -0.022 0.032 -0.693 0.488 0.978 0.919
## SEXnumeric -0.360 0.265 -1.356 0.175 0.698 0.415
## upper .95
## intercept 31.682
## arm 1.603
## TYPESTATUSnumeric 2.287
## day 2.067
## earlyacademicyear 1.103
## white 1.469
## structuraletiology 1.685
## priorepilepsy 1.062
## status 1.565
## ageyears 1.041
## SEXnumeric 1.174
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 184
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 115 | 69 |
## | 0.625 | 0.375 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 154
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 95 | 59 |
## | 0.617 | 0.383 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 30
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 10 |
## | 0.667 | 0.333 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstASMmore120min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstASMmore120min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.6834
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3140104 1.9539929
## sample estimates:
## odds ratio
## 0.8060165
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="no", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=120,
covariates= pSERG[pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 2.272 -11.336 15.879 0.744
## RMST (arm=1)/(arm=0) 1.027 0.872 1.210 0.746
## RMTL (arm=1)/(arm=0) 0.940 0.648 1.363 0.742
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 79.800 8.597 9.282 0.000 62.950 96.650
## arm 2.272 6.943 0.327 0.744 -11.336 15.879
## TYPESTATUSnumeric -20.854 5.358 -3.892 0.000 -31.355 -10.353
## day -3.333 5.367 -0.621 0.535 -13.853 7.187
## earlyacademicyear 3.497 5.374 0.651 0.515 -7.036 14.029
## white 1.691 5.551 0.305 0.761 -9.188 12.571
## structuraletiology -2.338 6.685 -0.350 0.727 -15.441 10.764
## priorepilepsy 9.059 5.604 1.616 0.106 -1.925 20.044
## status -1.338 6.614 -0.202 0.840 -14.302 11.626
## ageyears 0.529 0.539 0.980 0.327 -0.529 1.586
## SEXnumeric 4.151 5.440 0.763 0.445 -6.511 14.813
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.371 0.108 40.472 0.000 79.108 64.017
## arm 0.027 0.084 0.324 0.746 1.027 0.872
## TYPESTATUSnumeric -0.264 0.071 -3.725 0.000 0.768 0.668
## day -0.040 0.065 -0.610 0.542 0.961 0.846
## earlyacademicyear 0.044 0.065 0.671 0.502 1.045 0.919
## white 0.021 0.069 0.301 0.764 1.021 0.892
## structuraletiology -0.027 0.083 -0.325 0.745 0.973 0.828
## priorepilepsy 0.109 0.068 1.597 0.110 1.116 0.975
## status -0.015 0.081 -0.190 0.849 0.985 0.840
## ageyears 0.006 0.006 1.022 0.307 1.006 0.994
## SEXnumeric 0.051 0.067 0.762 0.446 1.052 0.923
## upper .95
## intercept 97.756
## arm 1.210
## TYPESTATUSnumeric 0.882
## day 1.092
## earlyacademicyear 1.187
## white 1.169
## structuraletiology 1.145
## priorepilepsy 1.276
## status 1.154
## ageyears 1.019
## SEXnumeric 1.200
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.651 0.224 16.321 0.000 38.497 24.833
## arm -0.062 0.190 -0.329 0.742 0.940 0.648
## TYPESTATUSnumeric 0.527 0.140 3.771 0.000 1.693 1.288
## day 0.092 0.146 0.628 0.530 1.096 0.824
## earlyacademicyear -0.086 0.148 -0.586 0.558 0.917 0.687
## white -0.045 0.143 -0.314 0.753 0.956 0.722
## structuraletiology 0.068 0.173 0.391 0.696 1.070 0.763
## priorepilepsy -0.247 0.153 -1.609 0.108 0.781 0.578
## status 0.044 0.177 0.250 0.802 1.045 0.739
## ageyears -0.015 0.017 -0.874 0.382 0.986 0.954
## SEXnumeric -0.108 0.145 -0.748 0.455 0.897 0.675
## upper .95
## intercept 59.680
## arm 1.363
## TYPESTATUSnumeric 2.226
## day 1.458
## earlyacademicyear 1.225
## white 1.266
## structuraletiology 1.501
## priorepilepsy 1.055
## status 1.477
## ageyears 1.018
## SEXnumeric 1.192
# First CI later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 78 |
## | 0.071 | 0.929 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 77
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 6 | 71 |
## | 0.078 | 0.922 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstCImore60min)
##
## 0 1
## 0 7
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore60min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore60min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.09426226 Inf
## sample estimates:
## odds ratio
## Inf
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.826 -0.620 2.271 0.263
## RMST (arm=1)/(arm=0) 1.014 0.989 1.039 0.270
## RMTL (arm=1)/(arm=0) 0.000 0.000 0.000 0.000
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 59.945 0.870 68.909 0.000 58.240 61.650
## arm 0.826 0.738 1.119 0.263 -0.620 2.271
## TYPESTATUSnumeric 0.491 0.641 0.765 0.444 -0.766 1.748
## day -0.101 1.089 -0.093 0.926 -2.235 2.033
## earlyacademicyear -1.916 1.096 -1.749 0.080 -4.064 0.231
## white -1.023 1.096 -0.934 0.350 -3.171 1.125
## structuraletiology 0.489 0.563 0.869 0.385 -0.614 1.592
## priorepilepsy -0.957 1.168 -0.819 0.413 -3.247 1.332
## status 1.491 0.940 1.586 0.113 -0.351 3.334
## ageyears -0.054 0.064 -0.837 0.403 -0.180 0.072
## SEXnumeric 1.636 1.455 1.125 0.261 -1.215 4.488
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.093 0.015 276.997 0.000 59.936 58.225
## arm 0.014 0.013 1.102 0.270 1.014 0.989
## TYPESTATUSnumeric 0.008 0.011 0.760 0.447 1.008 0.987
## day -0.002 0.019 -0.087 0.930 0.998 0.963
## earlyacademicyear -0.032 0.019 -1.723 0.085 0.968 0.933
## white -0.017 0.019 -0.925 0.355 0.983 0.947
## structuraletiology 0.008 0.010 0.847 0.397 1.008 0.989
## priorepilepsy -0.016 0.020 -0.812 0.417 0.984 0.946
## status 0.025 0.016 1.570 0.116 1.025 0.994
## ageyears -0.001 0.001 -0.832 0.405 0.999 0.997
## SEXnumeric 0.028 0.025 1.110 0.267 1.028 0.979
## upper .95
## intercept 61.697
## arm 1.039
## TYPESTATUSnumeric 1.030
## day 1.036
## earlyacademicyear 1.004
## white 1.020
## structuraletiology 1.027
## priorepilepsy 1.023
## status 1.058
## ageyears 1.001
## SEXnumeric 1.080
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept -3.964 2.740 -1.447 0.148 0.019 0.000
## arm -17.619 0.928 -18.986 0.000 0.000 0.000
## TYPESTATUSnumeric -1.291 1.369 -0.943 0.346 0.275 0.019
## day 1.144 1.206 0.949 0.343 3.140 0.296
## earlyacademicyear 3.138 1.190 2.638 0.008 23.052 2.240
## white 1.674 1.724 0.971 0.331 5.335 0.182
## structuraletiology -2.106 1.245 -1.692 0.091 0.122 0.011
## priorepilepsy 1.450 0.887 1.635 0.102 4.264 0.750
## status -18.128 1.231 -14.731 0.000 0.000 0.000
## ageyears 0.011 0.064 0.172 0.863 1.011 0.892
## SEXnumeric -1.889 1.159 -1.629 0.103 0.151 0.016
## upper .95
## intercept 4.079
## arm 0.000
## TYPESTATUSnumeric 4.021
## day 33.360
## earlyacademicyear 237.289
## white 156.600
## structuraletiology 1.396
## priorepilepsy 24.247
## status 0.000
## ageyears 1.146
## SEXnumeric 1.467
# First CI later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 64 |
## | 0.238 | 0.762 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 77
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 18 | 59 |
## | 0.234 | 0.766 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstCImore120min)
##
## 0 1
## 2 5
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore120min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore120min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.6689
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1129328 8.6849104
## sample estimates:
## odds ratio
## 0.7653212
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 4.913 -6.788 16.613 0.411
## RMST (arm=1)/(arm=0) 1.045 0.941 1.160 0.415
## RMTL (arm=1)/(arm=0) 0.523 0.102 2.684 0.438
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 110.300 8.310 13.273 0.000 94.013 126.587
## arm 4.913 5.970 0.823 0.411 -6.788 16.613
## TYPESTATUSnumeric 2.340 4.679 0.500 0.617 -6.832 11.511
## day -4.426 5.006 -0.884 0.377 -14.237 5.385
## earlyacademicyear -8.889 5.320 -1.671 0.095 -19.316 1.538
## white 6.523 5.294 1.232 0.218 -3.853 16.898
## structuraletiology 1.789 5.855 0.306 0.760 -9.687 13.266
## priorepilepsy -5.047 5.550 -0.909 0.363 -15.926 5.832
## status 10.101 4.748 2.127 0.033 0.794 19.407
## ageyears 0.120 0.444 0.270 0.787 -0.751 0.991
## SEXnumeric 0.826 5.200 0.159 0.874 -9.366 11.018
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.702 0.075 62.467 0.000 110.126 95.022
## arm 0.044 0.054 0.816 0.415 1.045 0.941
## TYPESTATUSnumeric 0.022 0.043 0.507 0.612 1.022 0.940
## day -0.041 0.046 -0.893 0.372 0.960 0.878
## earlyacademicyear -0.081 0.049 -1.646 0.100 0.922 0.837
## white 0.060 0.049 1.231 0.218 1.062 0.965
## structuraletiology 0.015 0.054 0.286 0.775 1.015 0.914
## priorepilepsy -0.046 0.051 -0.893 0.372 0.955 0.864
## status 0.090 0.043 2.104 0.035 1.095 1.006
## ageyears 0.001 0.004 0.262 0.794 1.001 0.993
## SEXnumeric 0.008 0.047 0.170 0.865 1.008 0.919
## upper .95
## intercept 127.631
## arm 1.160
## TYPESTATUSnumeric 1.111
## day 1.050
## earlyacademicyear 1.016
## white 1.168
## structuraletiology 1.128
## priorepilepsy 1.056
## status 1.191
## ageyears 1.009
## SEXnumeric 1.106
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.049 0.975 2.103 0.035 7.764 1.149
## arm -0.647 0.834 -0.776 0.438 0.523 0.102
## TYPESTATUSnumeric -0.215 0.505 -0.425 0.671 0.807 0.300
## day 0.380 0.506 0.752 0.452 1.463 0.543
## earlyacademicyear 0.945 0.592 1.596 0.111 2.573 0.806
## white -0.611 0.555 -1.101 0.271 0.543 0.183
## structuraletiology -0.255 0.596 -0.428 0.668 0.775 0.241
## priorepilepsy 0.527 0.513 1.026 0.305 1.694 0.619
## status -1.415 0.766 -1.847 0.065 0.243 0.054
## ageyears -0.015 0.046 -0.322 0.747 0.985 0.900
## SEXnumeric -0.017 0.558 -0.031 0.975 0.983 0.329
## upper .95
## intercept 52.442
## arm 2.684
## TYPESTATUSnumeric 2.172
## day 3.942
## earlyacademicyear 8.213
## white 1.611
## structuraletiology 2.490
## priorepilepsy 4.631
## status 1.090
## ageyears 1.079
## SEXnumeric 2.931
# First CI later than 240 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 48 | 36 |
## | 0.571 | 0.429 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 77
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 46 | 31 |
## | 0.597 | 0.403 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$HOSPITALONSET=="no" & pSERG$awareness2017 == 1, ]$firstCImore240min)
##
## 0 1
## 2 5
fisher.test(pSERG[pSERG$HOSPITALONSET=="no", ]$firstCImore240min, pSERG[pSERG$HOSPITALONSET=="no", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "no", ]$firstCImore240min and pSERG[pSERG$HOSPITALONSET == "no", ]$awareness2017
## p-value = 0.1326
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5550491 40.6682398
## sample estimates:
## odds ratio
## 3.652315
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", ]$awareness2017, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="no", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 28.511 -20.659 77.681 0.256
## RMST (arm=1)/(arm=0) 1.165 0.904 1.502 0.239
## RMTL (arm=1)/(arm=0) 0.583 0.181 1.875 0.365
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 185.450 26.115 7.101 0.000 134.266 236.634
## arm 28.511 25.087 1.136 0.256 -20.659 77.681
## TYPESTATUSnumeric -17.357 15.906 -1.091 0.275 -48.531 13.818
## day -8.354 15.591 -0.536 0.592 -38.911 22.203
## earlyacademicyear -27.603 15.269 -1.808 0.071 -57.529 2.323
## white 22.788 16.083 1.417 0.157 -8.735 54.311
## structuraletiology -3.952 17.806 -0.222 0.824 -38.851 30.947
## priorepilepsy 8.510 17.091 0.498 0.619 -24.988 42.008
## status 12.547 16.197 0.775 0.439 -19.198 44.293
## ageyears -0.384 1.411 -0.272 0.785 -3.149 2.381
## SEXnumeric -9.170 14.558 -0.630 0.529 -37.702 19.363
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.213 0.145 35.832 0.000 183.645 138.083
## arm 0.153 0.130 1.179 0.239 1.165 0.904
## TYPESTATUSnumeric -0.104 0.095 -1.089 0.276 0.902 0.748
## day -0.043 0.088 -0.492 0.623 0.958 0.807
## earlyacademicyear -0.156 0.087 -1.787 0.074 0.856 0.721
## white 0.131 0.095 1.379 0.168 1.140 0.946
## structuraletiology -0.023 0.102 -0.228 0.820 0.977 0.800
## priorepilepsy 0.048 0.095 0.503 0.615 1.049 0.871
## status 0.078 0.091 0.855 0.393 1.081 0.905
## ageyears -0.002 0.008 -0.266 0.790 0.998 0.982
## SEXnumeric -0.050 0.082 -0.607 0.544 0.951 0.809
## upper .95
## intercept 244.240
## arm 1.502
## TYPESTATUSnumeric 1.086
## day 1.137
## earlyacademicyear 1.015
## white 1.373
## structuraletiology 1.193
## priorepilepsy 1.263
## status 1.291
## ageyears 1.014
## SEXnumeric 1.118
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.927 0.451 8.716 0.000 50.778 20.995
## arm -0.540 0.596 -0.905 0.365 0.583 0.181
## TYPESTATUSnumeric 0.240 0.232 1.036 0.300 1.271 0.807
## day 0.165 0.261 0.632 0.527 1.180 0.707
## earlyacademicyear 0.448 0.255 1.758 0.079 1.565 0.950
## white -0.354 0.246 -1.440 0.150 0.702 0.434
## structuraletiology 0.053 0.275 0.191 0.848 1.054 0.615
## priorepilepsy -0.136 0.291 -0.467 0.640 0.873 0.493
## status -0.154 0.278 -0.551 0.581 0.858 0.497
## ageyears 0.006 0.023 0.266 0.790 1.006 0.962
## SEXnumeric 0.159 0.240 0.664 0.507 1.173 0.733
## upper .95
## intercept 122.811
## arm 1.875
## TYPESTATUSnumeric 2.003
## day 1.968
## earlyacademicyear 2.577
## white 1.136
## structuraletiology 1.807
## priorepilepsy 1.545
## status 1.480
## ageyears 1.052
## SEXnumeric 1.876
## IN THE HOSPITAL
# Patients in each group
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 66 | 18 |
## | 0.786 | 0.214 |
## |-----------|-----------|
##
##
##
##
# Time to first BZD
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 4.00 10.00 30.83 24.25 360.00
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0)
## [1] 62.38161
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 84 84 10 5 16
# Figure time to first BZD
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
# Time to first BZD depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 4.00 9.50 32.83 27.50 360.00
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1), ]$BZDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.25 12.50 23.50 20.75 205.00
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 46 24.4 24.2
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 38 19.6 19.8
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 0.00150 0.00518
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 0.00183 0.00518
##
## Chisq= 0 on 1 degrees of freedom, p= 0.9
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.9426513
# Figure time to first BZD by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,60), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first BZD (min)", ylab= "Cum. prob. having received first BZD")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first BZD
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$BZDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 84, number of events= 84
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.18923
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.06385
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.30148
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.41950
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.01746
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.03476
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.04428
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.09315
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.02157
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.07387
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.20831
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.93815
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.35186
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.52119
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.98269
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.96584
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.04528
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.09763
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.97866
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.07666
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.29369
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.30279
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.27727
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.25481
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.24425
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.26017
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.31139
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.35498
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.02309
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.25797
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.644
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.211
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.087
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.646
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.071
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.134
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.142
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.262
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.934
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.286
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.5194
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.8330
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.2769
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.0997 .
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.9430
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.8937
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.8869
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.7930
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.3502
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.7746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.2083
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.9381
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.3519
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.5212
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.9827
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.9658
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.0453
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.0976
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9787
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.0767
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.8276
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.0659
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.7397
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.6574
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.0176
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.0354
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.9567
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.9111
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0218
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.9288
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.6795
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.5182
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.7851
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.9232
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.6088
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.5800
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.5678
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.5474
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9354
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.6494
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 2.149
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.698
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.328
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 2.507
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.586
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.608
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.924
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.201
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.024
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.785
##
## Concordance= 0.593 (se = 0.04 )
## Rsquare= 0.092 (max possible= 0.999 )
## Likelihood ratio test= 8.13 on 10 df, p=0.6
## Wald test = 8.11 on 10 df, p=0.6
## Score (logrank) test = 8.26 on 10 df, p=0.6
# Time to first non-BZD AED
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 21.75 39.00 85.01 76.25 1419.00
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0)
## [1] 171.8644
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## 84 84 39 29 58
# Figure time to first non-BZD AED
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
# Time to first non-BZD AED depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.0 20.0 41.5 72.7 75.5 503.0
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1), ]$AEDTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.0 24.0 30.0 130.2 81.0 1419.0
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness, rho = 1)
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 46 22.9 24.2
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 38 20.1 18.8
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 0.0709 0.244
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 0.0912 0.244
##
## Chisq= 0.2 on 1 degrees of freedom, p= 0.6
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.6209923
# Figure time to first non-BZD AED by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,120), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first non-BZD ASM (min)", ylab= "Cum. prob. having received first non-BZD ASM")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first non-BZD AED
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$AEDTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 84, number of events= 84
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.20097
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.23346
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.51702
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.21865
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.08460
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.50860
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.31219
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.20665
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.03994
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.13215
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.22259
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.79179
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.67702
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.24440
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.91888
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.66296
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.36642
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.22955
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.96085
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.87621
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.29256
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.29715
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.27515
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.24998
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.25610
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.26648
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.29450
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.34116
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.02282
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.26308
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.687
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.786
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.879
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.875
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white -0.330
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.909
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.060
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.606
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -1.750
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale -0.502
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.4921
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.4321
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.0602 .
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.3818
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.7411
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.0563 .
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.2891
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.5447
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.0800 .
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.6154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.2226
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.7918
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.6770
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.2444
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.9189
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.6630
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.3664
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.2295
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9608
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.8762
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.8179
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.2630
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.5963
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.8036
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.0883
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.6013
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.7318
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.8133
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0407
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.1413
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.6891
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.4423
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.9780
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.7624
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.5562
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.9864
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.7672
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.6300
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9188
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.5232
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 2.169
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.418
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 2.876
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 2.031
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.518
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 2.804
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 2.434
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 2.400
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.005
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.467
##
## Concordance= 0.609 (se = 0.038 )
## Rsquare= 0.164 (max possible= 0.999 )
## Likelihood ratio test= 15.07 on 10 df, p=0.1
## Wald test = 15.01 on 10 df, p=0.1
## Score (logrank) test = 15.32 on 10 df, p=0.1
# Time to first CI
summary(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 122.0 210.0 569.9 495.0 7200.0 49
sd(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0)
## [1] NA
survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ 1)
## Call: survfit(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## 1)
##
## 49 observations deleted due to missingness
## n events median 0.95LCL 0.95UCL
## 35 35 210 165 420
# Figure time to first CI
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ 1), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("purple4"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
# Time to first CI depending on awareness
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 122.0 210.0 609.9 510.0 7200.0 37
summary(pSERG[which(pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1), ]$CONTTIME.0)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 120.0 146.0 204.5 376.5 372.5 1175.0 12
survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)
## Call:
## survdiff(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness, rho = 1)
##
## n=35, 49 observations deleted due to missingness.
##
## N Observed Expected
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 23 12.43 11.46
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 12 5.74 6.71
## (O-E)^2/E (O-E)^2/V
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=0 0.0824 0.337
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness=1 0.1405 0.337
##
## Chisq= 0.3 on 1 degrees of freedom, p= 0.6
pchisq(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$chisq, length(survdiff(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness, rho=1)$n)-1, lower.tail = FALSE)
## [1] 0.5615184
# Figure time to first CI by awareness
plot(survfit(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017), fun = "event",
conf.int = FALSE, xlim = c(0,500), col = c("violetred3", "seagreen3"), lwd = 3,
cex.axis = 1.3, cex.lab = 1.5,
frame.plot=FALSE,
xlab= "Time to first CI (min)", ylab= "Cum. prob. having received first CI")
legend("topleft", legend=c("2011-2016", "2017-2019"),
col=c("violetred3", "seagreen3"),
lty=1,
lwd=3,
horiz=FALSE,
bty='n',
cex=1.3)
########################## COX REGRESSION WITH ALL FACTORS OF INTEREST################################
## Time to first CI
summary(coxph(Surv(pSERG[pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0) ~ pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET=="yes", ]$TYPESTATUS +
pSERG[pSERG$HOSPITALONSET=="yes", ]$day + pSERG[pSERG$HOSPITALONSET=="yes", ]$earlyacademicyear + pSERG[pSERG$HOSPITALONSET=="yes", ]$white +
pSERG[pSERG$HOSPITALONSET=="yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET=="yes", ]$priorepilepsy +
pSERG[pSERG$HOSPITALONSET=="yes", ]$status + pSERG[pSERG$HOSPITALONSET=="yes", ]$ageyears + pSERG[pSERG$HOSPITALONSET=="yes", ]$SEX))
## Call:
## coxph(formula = Surv(pSERG[pSERG$HOSPITALONSET == "yes", ]$CONTTIME.0) ~
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$TYPESTATUS + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$day + pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear +
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$structuraletiology + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$priorepilepsy + pSERG[pSERG$HOSPITALONSET ==
## "yes", ]$status + pSERG[pSERG$HOSPITALONSET == "yes",
## ]$ageyears + pSERG[pSERG$HOSPITALONSET == "yes", ]$SEX)
##
## n= 35, number of events= 35
## (49 observations deleted due to missingness)
##
## coef
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.39419
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.47707
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.25278
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.48003
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.16398
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -0.62622
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.88920
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status -0.17897
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -0.05362
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.91754
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.48318
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.62060
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.28760
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.61613
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.17819
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.53461
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 2.43318
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.83613
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.94779
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 2.50313
## se(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.57675
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.53133
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.48161
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.42508
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.44513
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.49824
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.61925
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.81158
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.04333
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.47211
## z
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.683
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent -0.898
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.525
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.129
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.368
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology -1.257
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 1.436
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status -0.221
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears -1.238
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 1.943
## Pr(>|z|)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.494
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.369
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.600
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.259
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.713
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.209
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.151
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.825
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.216
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.052 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 1.4832
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.6206
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 1.2876
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 1.6161
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 1.1782
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.5346
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 2.4332
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.8361
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.9478
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 2.5031
## exp(-coef)
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.6742
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.6113
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.7766
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.6188
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.8488
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.8705
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.4110
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 1.1960
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.0551
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.3995
## lower .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 0.4789
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 0.2190
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 0.5010
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 0.7025
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 0.4924
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 0.2013
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 0.7229
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 0.1704
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 0.8706
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 0.9923
## upper .95
## pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017 4.593
## pSERG[pSERG$HOSPITALONSET == "yes", ]$TYPESTATUSintermittent 1.758
## pSERG[pSERG$HOSPITALONSET == "yes", ]$day 3.309
## pSERG[pSERG$HOSPITALONSET == "yes", ]$earlyacademicyear 3.718
## pSERG[pSERG$HOSPITALONSET == "yes", ]$white 2.819
## pSERG[pSERG$HOSPITALONSET == "yes", ]$structuraletiology 1.420
## pSERG[pSERG$HOSPITALONSET == "yes", ]$priorepilepsy 8.190
## pSERG[pSERG$HOSPITALONSET == "yes", ]$status 4.103
## pSERG[pSERG$HOSPITALONSET == "yes", ]$ageyears 1.032
## pSERG[pSERG$HOSPITALONSET == "yes", ]$SEXmale 6.315
##
## Concordance= 0.642 (se = 0.06 )
## Rsquare= 0.291 (max possible= 0.995 )
## Likelihood ratio test= 12.05 on 10 df, p=0.3
## Wald test = 10.55 on 10 df, p=0.4
## Score (logrank) test = 11.61 on 10 df, p=0.3
#### Recommendations and outliers in the hospital
# First BZD later than 20 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 57 | 27 |
## | 0.679 | 0.321 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 66
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 44 | 22 |
## | 0.667 | 0.333 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstBZDmore20min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 13 | 5 |
## | 0.722 | 0.278 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore20min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore20min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.7798
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1905202 2.6912865
## sample estimates:
## odds ratio
## 0.7715721
# Difference adjusting for covariates within the first 20 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=20,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 20 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.647 -2.858 4.152 0.718
## RMST (arm=1)/(arm=0) 1.071 0.787 1.456 0.663
## RMTL (arm=1)/(arm=0) 0.941 0.620 1.427 0.773
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 9.914 2.314 4.284 0.000 5.379 14.449
## arm 0.647 1.788 0.362 0.718 -2.858 4.152
## TYPESTATUSnumeric -0.099 1.775 -0.056 0.955 -3.579 3.381
## day -2.119 1.621 -1.307 0.191 -5.297 1.058
## earlyacademicyear -2.270 1.664 -1.364 0.172 -5.532 0.991
## white 0.988 1.675 0.590 0.555 -2.294 4.271
## structuraletiology -0.211 1.735 -0.121 0.903 -3.611 3.189
## priorepilepsy 1.761 1.862 0.946 0.344 -1.889 5.412
## status 0.427 1.992 0.214 0.830 -3.478 4.332
## ageyears 0.250 0.159 1.572 0.116 -0.062 0.563
## SEXnumeric 0.825 1.765 0.467 0.640 -2.636 4.285
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.286 0.215 10.644 0.000 9.834 6.455
## arm 0.068 0.157 0.435 0.663 1.071 0.787
## TYPESTATUSnumeric -0.021 0.167 -0.128 0.898 0.979 0.705
## day -0.178 0.146 -1.220 0.222 0.837 0.629
## earlyacademicyear -0.205 0.155 -1.323 0.186 0.815 0.602
## white 0.082 0.160 0.515 0.607 1.086 0.794
## structuraletiology -0.012 0.160 -0.073 0.941 0.988 0.722
## priorepilepsy 0.149 0.166 0.898 0.369 1.160 0.839
## status 0.055 0.164 0.335 0.737 1.056 0.766
## ageyears 0.021 0.014 1.559 0.119 1.022 0.995
## SEXnumeric 0.057 0.162 0.352 0.725 1.059 0.770
## upper .95
## intercept 14.981
## arm 1.456
## TYPESTATUSnumeric 1.359
## day 1.114
## earlyacademicyear 1.104
## white 1.485
## structuraletiology 1.354
## priorepilepsy 1.605
## status 1.456
## ageyears 1.050
## SEXnumeric 1.455
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.298 0.264 8.719 0.000 9.957 5.940
## arm -0.061 0.213 -0.288 0.773 0.941 0.620
## TYPESTATUSnumeric -0.007 0.199 -0.033 0.974 0.994 0.673
## day 0.266 0.191 1.394 0.163 1.305 0.898
## earlyacademicyear 0.263 0.191 1.375 0.169 1.301 0.894
## white -0.128 0.184 -0.695 0.487 0.880 0.613
## structuraletiology 0.035 0.193 0.180 0.857 1.035 0.709
## priorepilepsy -0.221 0.232 -0.955 0.340 0.802 0.509
## status -0.021 0.269 -0.080 0.936 0.979 0.577
## ageyears -0.031 0.020 -1.514 0.130 0.970 0.932
## SEXnumeric -0.125 0.201 -0.622 0.534 0.882 0.595
## upper .95
## intercept 16.692
## arm 1.427
## TYPESTATUSnumeric 1.467
## day 1.896
## earlyacademicyear 1.891
## white 1.263
## structuraletiology 1.512
## priorepilepsy 1.262
## status 1.659
## ageyears 1.009
## SEXnumeric 1.309
# First BZD later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 69 | 15 |
## | 0.821 | 0.179 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 66
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 52 | 14 |
## | 0.788 | 0.212 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstBZDmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 17 | 1 |
## | 0.944 | 0.056 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore40min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore40min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.1736
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.004888634 1.672955447
## sample estimates:
## odds ratio
## 0.2213607
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -2.210 -8.115 3.695 0.463
## RMST (arm=1)/(arm=0) 0.896 0.596 1.346 0.596
## RMTL (arm=1)/(arm=0) 1.108 0.872 1.406 0.402
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 15.264 4.723 3.232 0.001 6.006 24.521
## arm -2.210 3.013 -0.733 0.463 -8.115 3.695
## TYPESTATUSnumeric -3.018 2.923 -1.033 0.302 -8.747 2.711
## day -2.966 3.076 -0.964 0.335 -8.995 3.062
## earlyacademicyear -4.431 3.050 -1.453 0.146 -10.409 1.547
## white 1.767 3.225 0.548 0.584 -4.554 8.088
## structuraletiology 0.437 3.272 0.134 0.894 -5.975 6.849
## priorepilepsy 2.118 3.656 0.579 0.562 -5.048 9.283
## status -0.923 3.879 -0.238 0.812 -8.526 6.680
## ageyears 0.631 0.294 2.149 0.032 0.055 1.207
## SEXnumeric -1.560 3.091 -0.505 0.614 -7.618 4.498
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.683 0.320 8.397 0.000 14.626 7.819
## arm -0.110 0.208 -0.531 0.596 0.896 0.596
## TYPESTATUSnumeric -0.222 0.216 -1.026 0.305 0.801 0.525
## day -0.174 0.192 -0.908 0.364 0.840 0.577
## earlyacademicyear -0.282 0.199 -1.418 0.156 0.754 0.511
## white 0.102 0.213 0.479 0.632 1.107 0.730
## structuraletiology 0.047 0.210 0.224 0.823 1.048 0.695
## priorepilepsy 0.126 0.221 0.571 0.568 1.135 0.736
## status -0.020 0.224 -0.088 0.930 0.980 0.632
## ageyears 0.038 0.017 2.231 0.026 1.039 1.005
## SEXnumeric -0.121 0.198 -0.608 0.543 0.886 0.601
## upper .95
## intercept 27.357
## arm 1.346
## TYPESTATUSnumeric 1.223
## day 1.223
## earlyacademicyear 1.114
## white 1.680
## structuraletiology 1.580
## priorepilepsy 1.750
## status 1.522
## ageyears 1.075
## SEXnumeric 1.308
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.192 0.196 16.305 0.000 24.344 16.586
## arm 0.102 0.122 0.838 0.402 1.108 0.872
## TYPESTATUSnumeric 0.117 0.116 1.011 0.312 1.124 0.896
## day 0.130 0.132 0.981 0.327 1.138 0.879
## earlyacademicyear 0.185 0.129 1.433 0.152 1.204 0.934
## white -0.080 0.136 -0.592 0.554 0.923 0.707
## structuraletiology -0.011 0.136 -0.078 0.938 0.989 0.758
## priorepilepsy -0.092 0.162 -0.563 0.573 0.913 0.664
## status 0.053 0.177 0.301 0.763 1.055 0.745
## ageyears -0.027 0.014 -1.978 0.048 0.973 0.947
## SEXnumeric 0.054 0.129 0.419 0.675 1.055 0.820
## upper .95
## intercept 35.732
## arm 1.406
## TYPESTATUSnumeric 1.411
## day 1.475
## earlyacademicyear 1.551
## white 1.204
## structuraletiology 1.292
## priorepilepsy 1.255
## status 1.493
## ageyears 1.000
## SEXnumeric 1.358
# First BZD later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 73 | 11 |
## | 0.869 | 0.131 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 66
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 56 | 10 |
## | 0.848 | 0.152 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstBZDmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 17 | 1 |
## | 0.944 | 0.056 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstBZDmore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstBZDmore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.4428
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.007178925 2.657237489
## sample estimates:
## odds ratio
## 0.3327754
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$BZDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -4.671 -12.560 3.218 0.246
## RMST (arm=1)/(arm=0) 0.794 0.486 1.295 0.355
## RMTL (arm=1)/(arm=0) 1.124 0.933 1.354 0.219
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 22.536 6.843 3.294 0.001 9.125 35.948
## arm -4.671 4.025 -1.160 0.246 -12.560 3.218
## TYPESTATUSnumeric -4.372 3.820 -1.144 0.252 -11.859 3.115
## day -3.494 4.267 -0.819 0.413 -11.857 4.870
## earlyacademicyear -8.395 4.212 -1.993 0.046 -16.650 -0.140
## white 0.495 4.633 0.107 0.915 -8.585 9.576
## structuraletiology 0.416 4.406 0.094 0.925 -8.220 9.053
## priorepilepsy 0.580 5.006 0.116 0.908 -9.231 10.391
## status -1.663 4.985 -0.334 0.739 -11.432 8.107
## ageyears 0.887 0.417 2.129 0.033 0.070 1.704
## SEXnumeric -4.452 4.358 -1.022 0.307 -12.994 4.090
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.081 0.373 8.250 0.000 21.785 10.478
## arm -0.231 0.250 -0.925 0.355 0.794 0.486
## TYPESTATUSnumeric -0.269 0.254 -1.061 0.289 0.764 0.465
## day -0.188 0.219 -0.857 0.391 0.829 0.540
## earlyacademicyear -0.452 0.224 -2.017 0.044 0.636 0.410
## white 0.005 0.247 0.021 0.983 1.005 0.619
## structuraletiology 0.049 0.239 0.204 0.839 1.050 0.657
## priorepilepsy 0.025 0.252 0.101 0.920 1.026 0.626
## status -0.052 0.252 -0.208 0.836 0.949 0.580
## ageyears 0.045 0.020 2.290 0.022 1.046 1.007
## SEXnumeric -0.254 0.230 -1.106 0.269 0.775 0.494
## upper .95
## intercept 45.294
## arm 1.295
## TYPESTATUSnumeric 1.256
## day 1.273
## earlyacademicyear 0.987
## white 1.632
## structuraletiology 1.678
## priorepilepsy 1.680
## status 1.554
## ageyears 1.087
## SEXnumeric 1.217
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.616 0.172 21.065 0.000 37.187 26.563
## arm 0.117 0.095 1.228 0.219 1.124 0.933
## TYPESTATUSnumeric 0.103 0.089 1.156 0.248 1.108 0.931
## day 0.085 0.108 0.788 0.431 1.089 0.881
## earlyacademicyear 0.206 0.109 1.897 0.058 1.229 0.993
## white -0.017 0.116 -0.143 0.886 0.984 0.784
## structuraletiology -0.005 0.107 -0.048 0.962 0.995 0.807
## priorepilepsy -0.014 0.127 -0.113 0.910 0.986 0.768
## status 0.046 0.128 0.361 0.718 1.047 0.815
## ageyears -0.022 0.011 -1.970 0.049 0.978 0.957
## SEXnumeric 0.104 0.108 0.962 0.336 1.110 0.898
## upper .95
## intercept 52.061
## arm 1.354
## TYPESTATUSnumeric 1.319
## day 1.345
## earlyacademicyear 1.520
## white 1.234
## structuraletiology 1.227
## priorepilepsy 1.265
## status 1.346
## ageyears 1.000
## SEXnumeric 1.371
# First non-BZD ASM later than 40 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 43 | 41 |
## | 0.512 | 0.488 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 66
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 32 | 34 |
## | 0.485 | 0.515 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstASMmore40min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 11 | 7 |
## | 0.611 | 0.389 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore40min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore40min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.4287
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1748355 1.9500290
## sample estimates:
## odds ratio
## 0.6025918
# Difference adjusting for covariates within the first 40 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=40,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 40 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -0.152 -4.755 4.451 0.948
## RMST (arm=1)/(arm=0) 0.997 0.858 1.160 0.973
## RMTL (arm=1)/(arm=0) 1.043 0.626 1.737 0.872
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 28.692 3.749 7.653 0.000 21.344 36.040
## arm -0.152 2.348 -0.065 0.948 -4.755 4.451
## TYPESTATUSnumeric 1.943 3.033 0.641 0.522 -4.001 7.887
## day -4.723 2.558 -1.846 0.065 -9.737 0.292
## earlyacademicyear -0.288 2.728 -0.106 0.916 -5.634 5.058
## white 4.010 2.545 1.576 0.115 -0.978 8.997
## structuraletiology -1.498 2.610 -0.574 0.566 -6.613 3.617
## priorepilepsy -0.025 2.784 -0.009 0.993 -5.482 5.431
## status -1.818 3.402 -0.534 0.593 -8.487 4.851
## ageyears 0.437 0.245 1.780 0.075 -0.044 0.918
## SEXnumeric -0.585 2.933 -0.199 0.842 -6.333 5.164
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.353 0.126 26.653 0.000 28.597 22.348
## arm -0.003 0.077 -0.033 0.973 0.997 0.858
## TYPESTATUSnumeric 0.066 0.100 0.656 0.512 1.068 0.877
## day -0.155 0.086 -1.806 0.071 0.856 0.724
## earlyacademicyear -0.007 0.091 -0.081 0.936 0.993 0.830
## white 0.134 0.088 1.523 0.128 1.143 0.962
## structuraletiology -0.050 0.089 -0.566 0.572 0.951 0.798
## priorepilepsy -0.004 0.088 -0.041 0.968 0.996 0.839
## status -0.059 0.113 -0.523 0.601 0.943 0.756
## ageyears 0.014 0.008 1.757 0.079 1.014 0.998
## SEXnumeric -0.023 0.099 -0.232 0.817 0.977 0.805
## upper .95
## intercept 36.595
## arm 1.160
## TYPESTATUSnumeric 1.300
## day 1.013
## earlyacademicyear 1.187
## white 1.358
## structuraletiology 1.132
## priorepilepsy 1.184
## status 1.176
## ageyears 1.030
## SEXnumeric 1.186
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 2.403 0.396 6.073 0.000 11.053 5.090
## arm 0.042 0.260 0.161 0.872 1.043 0.626
## TYPESTATUSnumeric -0.187 0.324 -0.576 0.565 0.830 0.440
## day 0.512 0.280 1.830 0.067 1.669 0.964
## earlyacademicyear 0.050 0.275 0.181 0.856 1.051 0.613
## white -0.419 0.262 -1.601 0.109 0.657 0.393
## structuraletiology 0.148 0.251 0.590 0.555 1.159 0.709
## priorepilepsy -0.018 0.358 -0.049 0.961 0.982 0.487
## status 0.201 0.379 0.531 0.595 1.223 0.582
## ageyears -0.052 0.032 -1.633 0.102 0.949 0.891
## SEXnumeric 0.016 0.296 0.053 0.958 1.016 0.568
## upper .95
## intercept 24.003
## arm 1.737
## TYPESTATUSnumeric 1.566
## day 2.888
## earlyacademicyear 1.803
## white 1.099
## structuraletiology 1.895
## priorepilepsy 1.980
## status 2.572
## ageyears 1.011
## SEXnumeric 1.815
# First non-BZD ASM later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 56 | 28 |
## | 0.667 | 0.333 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 66
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 43 | 23 |
## | 0.652 | 0.348 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstASMmore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 13 | 5 |
## | 0.722 | 0.278 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.7788
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1786629 2.5087283
## sample estimates:
## odds ratio
## 0.7218025
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=60,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -3.333 -11.602 4.935 0.429
## RMST (arm=1)/(arm=0) 0.917 0.736 1.141 0.436
## RMTL (arm=1)/(arm=0) 1.169 0.795 1.719 0.429
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 38.112 6.375 5.978 0.000 25.617 50.607
## arm -3.333 4.219 -0.790 0.429 -11.602 4.935
## TYPESTATUSnumeric -0.593 5.091 -0.116 0.907 -10.570 9.385
## day -8.585 4.327 -1.984 0.047 -17.066 -0.104
## earlyacademicyear 1.169 4.495 0.260 0.795 -7.641 9.979
## white 6.444 4.407 1.462 0.144 -2.194 15.082
## structuraletiology -5.796 4.203 -1.379 0.168 -14.033 2.442
## priorepilepsy -1.489 4.807 -0.310 0.757 -10.912 7.933
## status -3.432 5.487 -0.625 0.532 -14.186 7.323
## ageyears 0.720 0.402 1.789 0.074 -0.069 1.509
## SEXnumeric 1.394 4.718 0.296 0.768 -7.852 10.640
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.636 0.167 21.749 0.000 37.952 27.347
## arm -0.087 0.112 -0.779 0.436 0.917 0.736
## TYPESTATUSnumeric -0.014 0.135 -0.103 0.918 0.986 0.756
## day -0.222 0.114 -1.950 0.051 0.801 0.641
## earlyacademicyear 0.037 0.118 0.311 0.756 1.038 0.822
## white 0.169 0.121 1.399 0.162 1.184 0.934
## structuraletiology -0.160 0.114 -1.401 0.161 0.852 0.681
## priorepilepsy -0.043 0.120 -0.360 0.719 0.958 0.758
## status -0.095 0.146 -0.653 0.514 0.909 0.682
## ageyears 0.017 0.010 1.764 0.078 1.018 0.998
## SEXnumeric 0.034 0.122 0.274 0.784 1.034 0.813
## upper .95
## intercept 52.668
## arm 1.141
## TYPESTATUSnumeric 1.286
## day 1.001
## earlyacademicyear 1.309
## white 1.502
## structuraletiology 1.066
## priorepilepsy 1.211
## status 1.211
## ageyears 1.038
## SEXnumeric 1.315
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.077 0.304 10.119 0.000 21.697 11.955
## arm 0.156 0.197 0.792 0.429 1.169 0.795
## TYPESTATUSnumeric 0.032 0.232 0.139 0.889 1.033 0.655
## day 0.417 0.216 1.933 0.053 1.518 0.994
## earlyacademicyear -0.038 0.209 -0.181 0.856 0.963 0.639
## white -0.306 0.203 -1.509 0.131 0.736 0.495
## structuraletiology 0.248 0.192 1.292 0.196 1.282 0.880
## priorepilepsy 0.066 0.256 0.257 0.797 1.068 0.647
## status 0.139 0.261 0.531 0.596 1.149 0.688
## ageyears -0.039 0.023 -1.695 0.090 0.961 0.919
## SEXnumeric -0.082 0.226 -0.363 0.717 0.921 0.592
## upper .95
## intercept 39.378
## arm 1.719
## TYPESTATUSnumeric 1.629
## day 2.317
## earlyacademicyear 1.451
## white 1.096
## structuraletiology 1.868
## priorepilepsy 1.762
## status 1.917
## ageyears 1.006
## SEXnumeric 1.434
# First non-BZD ASM later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 84
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 72 | 12 |
## | 0.857 | 0.143 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 66
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 57 | 9 |
## | 0.864 | 0.136 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstASMmore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 18
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 15 | 3 |
## | 0.833 | 0.167 |
## |-----------|-----------|
##
##
##
##
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstASMmore120min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstASMmore120min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.7143
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1959377 5.9466903
## sample estimates:
## odds ratio
## 1.262937
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[pSERG$HOSPITALONSET=="yes", ]$AEDTIME.0, status=pSERG[pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=120,
covariates= pSERG[pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) -5.185 -22.741 12.371 0.563
## RMST (arm=1)/(arm=0) 0.897 0.635 1.267 0.537
## RMTL (arm=1)/(arm=0) 1.074 0.828 1.393 0.592
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 57.049 13.547 4.211 0.000 30.497 83.600
## arm -5.185 8.957 -0.579 0.563 -22.741 12.371
## TYPESTATUSnumeric -11.448 8.409 -1.361 0.173 -27.929 5.033
## day -15.583 8.285 -1.881 0.060 -31.821 0.654
## earlyacademicyear -2.209 8.506 -0.260 0.795 -18.881 14.463
## white 6.401 8.641 0.741 0.459 -10.535 23.337
## structuraletiology -12.940 7.630 -1.696 0.090 -27.896 2.015
## priorepilepsy -4.295 9.722 -0.442 0.659 -23.348 14.759
## status -3.488 10.936 -0.319 0.750 -24.922 17.947
## ageyears 1.550 0.776 1.997 0.046 0.028 3.072
## SEXnumeric 4.885 8.321 0.587 0.557 -11.424 21.194
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.041 0.259 15.626 0.000 56.902 34.276
## arm -0.109 0.176 -0.617 0.537 0.897 0.635
## TYPESTATUSnumeric -0.241 0.182 -1.320 0.187 0.786 0.550
## day -0.289 0.158 -1.830 0.067 0.749 0.549
## earlyacademicyear -0.033 0.169 -0.192 0.847 0.968 0.695
## white 0.122 0.175 0.693 0.488 1.129 0.801
## structuraletiology -0.271 0.159 -1.699 0.089 0.763 0.558
## priorepilepsy -0.095 0.184 -0.516 0.606 0.909 0.634
## status -0.071 0.219 -0.323 0.747 0.932 0.607
## ageyears 0.027 0.013 2.023 0.043 1.027 1.001
## SEXnumeric 0.089 0.155 0.574 0.566 1.093 0.806
## upper .95
## intercept 94.466
## arm 1.267
## TYPESTATUSnumeric 1.124
## day 1.021
## earlyacademicyear 1.348
## white 1.593
## structuraletiology 1.042
## priorepilepsy 1.304
## status 1.430
## ageyears 1.055
## SEXnumeric 1.483
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.141 0.205 20.206 0.000 62.861 42.067
## arm 0.071 0.133 0.536 0.592 1.074 0.828
## TYPESTATUSnumeric 0.160 0.118 1.362 0.173 1.174 0.932
## day 0.240 0.130 1.848 0.065 1.271 0.986
## earlyacademicyear 0.037 0.126 0.298 0.766 1.038 0.812
## white -0.098 0.127 -0.771 0.441 0.907 0.707
## structuraletiology 0.182 0.111 1.642 0.101 1.200 0.965
## priorepilepsy 0.060 0.151 0.400 0.689 1.062 0.791
## status 0.048 0.163 0.291 0.771 1.049 0.762
## ageyears -0.025 0.013 -1.883 0.060 0.975 0.950
## SEXnumeric -0.078 0.127 -0.611 0.541 0.925 0.721
## upper .95
## intercept 93.933
## arm 1.393
## TYPESTATUSnumeric 1.478
## day 1.639
## earlyacademicyear 1.328
## white 1.163
## structuraletiology 1.491
## priorepilepsy 1.426
## status 1.444
## ageyears 1.001
## SEXnumeric 1.188
# First CI later than 60 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 32 |
## | 0.086 | 0.914 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstCImore60min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 29
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 3 | 26 |
## | 0.103 | 0.897 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstCImore60min)
##
## 0 1
## 0 6
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore60min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore60min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.07862118 Inf
## sample estimates:
## odds ratio
## Inf
# Difference adjusting for covariates within the first 60 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=60,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
## Warning in sqrt(diag(varbeta)): NaNs produced
##
## The truncation time: tau = 60 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 3.237 -2.847 9.321 0.297
## RMST (arm=1)/(arm=0) 1.055 0.949 1.172 0.323
## RMTL (arm=1)/(arm=0) 0.000 NaN NaN NaN
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 57.833 3.896 14.843 0.000 50.196 65.470
## arm 3.237 3.104 1.043 0.297 -2.847 9.321
## TYPESTATUSnumeric 5.332 5.913 0.902 0.367 -6.256 16.921
## day -4.901 3.869 -1.267 0.205 -12.485 2.683
## earlyacademicyear -1.560 3.193 -0.489 0.625 -7.819 4.698
## white -3.362 3.946 -0.852 0.394 -11.097 4.372
## structuraletiology 3.051 4.719 0.647 0.518 -6.198 12.300
## priorepilepsy 2.926 3.501 0.836 0.403 -3.935 9.787
## status 3.447 4.782 0.721 0.471 -5.925 12.819
## ageyears 0.367 0.399 0.920 0.358 -0.414 1.148
## SEXnumeric -5.557 4.155 -1.338 0.181 -13.700 2.586
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.052 0.075 54.184 0.000 57.511 49.670
## arm 0.053 0.054 0.988 0.323 1.055 0.949
## TYPESTATUSnumeric 0.097 0.114 0.848 0.397 1.102 0.881
## day -0.091 0.077 -1.170 0.242 0.913 0.785
## earlyacademicyear -0.026 0.058 -0.453 0.650 0.974 0.870
## white -0.056 0.070 -0.797 0.425 0.946 0.824
## structuraletiology 0.054 0.085 0.639 0.523 1.056 0.893
## priorepilepsy 0.047 0.063 0.737 0.461 1.048 0.926
## status 0.065 0.093 0.699 0.484 1.067 0.890
## ageyears 0.007 0.008 0.881 0.378 1.007 0.992
## SEXnumeric -0.098 0.076 -1.278 0.201 0.907 0.781
## upper .95
## intercept 66.589
## arm 1.172
## TYPESTATUSnumeric 1.379
## day 1.063
## earlyacademicyear 1.091
## white 1.085
## structuraletiology 1.248
## priorepilepsy 1.186
## status 1.280
## ageyears 1.022
## SEXnumeric 1.054
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef)
## intercept -189.306 NaN NaN NaN 0.000000e+00
## arm -28.451 NaN NaN NaN 0.000000e+00
## TYPESTATUSnumeric -41.708 NaN NaN NaN 0.000000e+00
## day 81.136 NaN NaN NaN 1.725126e+35
## earlyacademicyear 64.796 NaN NaN NaN 1.382580e+28
## white 85.305 NaN NaN NaN 1.115588e+37
## structuraletiology 18.306 0.938 19.515 0.00 8.919254e+07
## priorepilepsy -64.355 0.704 -91.424 0.00 0.000000e+00
## status -27.650 NaN NaN NaN 0.000000e+00
## ageyears 0.242 0.152 1.597 0.11 1.274000e+00
## SEXnumeric 26.327 NaN NaN NaN 2.714457e+11
## lower .95 upper .95
## intercept NaN NaN
## arm NaN NaN
## TYPESTATUSnumeric NaN NaN
## day NaN NaN
## earlyacademicyear NaN NaN
## white NaN NaN
## structuraletiology 14185531.039 5.608044e+08
## priorepilepsy 0.000 0.000000e+00
## status NaN NaN
## ageyears 0.946 1.716000e+00
## SEXnumeric NaN NaN
# First CI later than 120 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 8 | 27 |
## | 0.229 | 0.771 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstCImore120min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 29
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 7 | 22 |
## | 0.241 | 0.759 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstCImore120min)
##
## 0 1
## 1 5
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore120min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore120min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1369372 85.8104846
## sample estimates:
## odds ratio
## 1.571737
# Difference adjusting for covariates within the first 120 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=120,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
## Warning in sqrt(diag(varbeta)): NaNs produced
##
## The truncation time: tau = 120 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 8.283 -6.148 22.713 0.261
## RMST (arm=1)/(arm=0) 1.072 0.943 1.218 0.288
## RMTL (arm=1)/(arm=0) 0.000 0.000 7068292.932 0.220
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 117.436 8.836 13.291 0.000 100.118 134.755
## arm 8.283 7.363 1.125 0.261 -6.148 22.713
## TYPESTATUSnumeric 9.063 14.273 0.635 0.525 -18.911 37.038
## day -11.561 9.252 -1.250 0.211 -29.695 6.572
## earlyacademicyear -6.026 8.294 -0.727 0.468 -22.281 10.230
## white -6.927 9.358 -0.740 0.459 -25.268 11.414
## structuraletiology 4.212 11.192 0.376 0.707 -17.724 26.148
## priorepilepsy 8.735 8.740 0.999 0.318 -8.396 25.866
## status 6.581 11.265 0.584 0.559 -15.499 28.661
## ageyears 0.636 0.868 0.733 0.464 -1.065 2.337
## SEXnumeric -12.726 9.257 -1.375 0.169 -30.869 5.416
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 4.759 0.085 56.299 0.000 116.599 98.797
## arm 0.069 0.065 1.062 0.288 1.072 0.943
## TYPESTATUSnumeric 0.083 0.140 0.590 0.555 1.086 0.826
## day -0.108 0.094 -1.148 0.251 0.898 0.747
## earlyacademicyear -0.052 0.076 -0.685 0.493 0.949 0.817
## white -0.057 0.086 -0.660 0.509 0.945 0.798
## structuraletiology 0.039 0.103 0.378 0.705 1.040 0.850
## priorepilepsy 0.071 0.082 0.870 0.384 1.074 0.914
## status 0.064 0.113 0.564 0.573 1.066 0.854
## ageyears 0.006 0.009 0.720 0.471 1.006 0.989
## SEXnumeric -0.114 0.088 -1.298 0.194 0.892 0.750
## upper .95
## intercept 137.607
## arm 1.218
## TYPESTATUSnumeric 1.429
## day 1.079
## earlyacademicyear 1.102
## white 1.118
## structuraletiology 1.271
## priorepilepsy 1.262
## status 1.330
## ageyears 1.023
## SEXnumeric 1.060
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef)
## intercept -144.348 36.616 -3.942 0.000 0.000000e+00
## arm -26.350 21.491 -1.226 0.220 0.000000e+00
## TYPESTATUSnumeric -22.649 0.197 -114.936 0.000 0.000000e+00
## day 62.122 2.871 21.638 0.000 9.535760e+26
## earlyacademicyear 43.361 0.461 94.022 0.000 6.786335e+18
## white 63.198 NaN NaN NaN 2.796426e+27
## structuraletiology 18.255 0.989 18.463 0.000 8.472256e+07
## priorepilepsy -45.442 1.215 -37.391 0.000 0.000000e+00
## status -22.914 3.417 -6.705 0.000 0.000000e+00
## ageyears 0.203 0.141 1.438 0.151 1.225000e+00
## SEXnumeric 23.316 1.556 14.981 0.000 1.336374e+10
## lower .95 upper .95
## intercept 0.000000e+00 0.000000e+00
## arm 0.000000e+00 7.068293e+06
## TYPESTATUSnumeric 0.000000e+00 0.000000e+00
## day 3.432012e+24 2.649487e+29
## earlyacademicyear 2.748373e+18 1.675695e+19
## white NaN NaN
## structuraletiology 1.220139e+07 5.882863e+08
## priorepilepsy 0.000000e+00 0.000000e+00
## status 0.000000e+00 0.000000e+00
## ageyears 9.290000e-01 1.616000e+00
## SEXnumeric 6.326410e+08 2.822922e+11
# First CI later than 240 minutes
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 20 | 15 |
## | 0.571 | 0.429 |
## |-----------|-----------|
##
##
##
##
CrossTable(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 0, ]$firstCImore240min)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 29
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 16 | 13 |
## | 0.552 | 0.448 |
## |-----------|-----------|
##
##
##
##
table(pSERG[pSERG$HOSPITALONSET=="yes" & pSERG$awareness2017 == 1, ]$firstCImore240min)
##
## 0 1
## 4 2
fisher.test(pSERG[pSERG$HOSPITALONSET=="yes", ]$firstCImore240min, pSERG[pSERG$HOSPITALONSET=="yes", ]$awareness2017, alternative = "two.sided")
##
## Fisher's Exact Test for Count Data
##
## data: pSERG[pSERG$HOSPITALONSET == "yes", ]$firstCImore240min and pSERG[pSERG$HOSPITALONSET == "yes", ]$awareness2017
## p-value = 0.6804
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.04905162 5.18455750
## sample estimates:
## odds ratio
## 0.623748
# Difference adjusting for covariates within the first 240 minutes
rmst2(time=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$CONTTIME.0, status=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$event, arm=pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", ]$awareness2017, tau=240,
covariates= pSERG[!is.na(pSERG$CONTTIME.0) & pSERG$HOSPITALONSET=="yes", c("TYPESTATUSnumeric", "day",
"earlyacademicyear", "white", "structuraletiology",
"priorepilepsy", "status", "ageyears", "SEXnumeric")])
##
## The truncation time: tau = 240 was specified.
##
## Summary of between-group contrast (adjusted for the covariates)
## Est. lower .95 upper .95 p
## RMST (arm=1)-(arm=0) 0.165 -52.080 52.411 0.995
## RMST (arm=1)/(arm=0) 1.002 0.754 1.330 0.991
## RMTL (arm=1)/(arm=0) 1.024 0.366 2.860 0.965
##
##
## Model summary (difference of RMST)
## coef se(coef) z p lower .95 upper .95
## intercept 211.320 31.666 6.673 0.000 149.256 273.384
## arm 0.165 26.656 0.006 0.995 -52.080 52.411
## TYPESTATUSnumeric -7.193 32.352 -0.222 0.824 -70.602 56.216
## day -23.496 24.915 -0.943 0.346 -72.329 25.337
## earlyacademicyear -19.437 25.020 -0.777 0.437 -68.475 29.601
## white -20.455 26.475 -0.773 0.440 -72.346 31.436
## structuraletiology 25.614 28.465 0.900 0.368 -30.176 81.404
## priorepilepsy -14.641 27.349 -0.535 0.592 -68.243 38.962
## status 16.861 36.812 0.458 0.647 -55.288 89.010
## ageyears 2.612 1.825 1.431 0.152 -0.965 6.189
## SEXnumeric -39.356 24.742 -1.591 0.112 -87.850 9.137
##
##
## Model summary (ratio of RMST)
## coef se(coef) z p exp(coef) lower .95
## intercept 5.348 0.175 30.473 0.000 210.110 148.960
## arm 0.002 0.145 0.011 0.991 1.002 0.754
## TYPESTATUSnumeric -0.035 0.201 -0.173 0.862 0.966 0.651
## day -0.136 0.142 -0.959 0.338 0.873 0.660
## earlyacademicyear -0.106 0.141 -0.752 0.452 0.899 0.682
## white -0.105 0.148 -0.708 0.479 0.900 0.673
## structuraletiology 0.139 0.156 0.890 0.374 1.149 0.846
## priorepilepsy -0.079 0.160 -0.492 0.623 0.924 0.676
## status 0.092 0.217 0.425 0.671 1.097 0.717
## ageyears 0.014 0.011 1.365 0.172 1.015 0.994
## SEXnumeric -0.210 0.143 -1.464 0.143 0.811 0.613
## upper .95
## intercept 296.363
## arm 1.330
## TYPESTATUSnumeric 1.433
## day 1.153
## earlyacademicyear 1.186
## white 1.204
## structuraletiology 1.559
## priorepilepsy 1.264
## status 1.678
## ageyears 1.036
## SEXnumeric 1.074
##
##
## Model summary (ratio of time-lost)
## coef se(coef) z p exp(coef) lower .95
## intercept 3.279 0.625 5.242 0.000 26.548 7.791
## arm 0.023 0.524 0.045 0.965 1.024 0.366
## TYPESTATUSnumeric 0.206 0.436 0.473 0.637 1.229 0.522
## day 0.368 0.505 0.729 0.466 1.445 0.537
## earlyacademicyear 0.372 0.436 0.852 0.394 1.450 0.617
## white 0.468 0.502 0.933 0.351 1.597 0.598
## structuraletiology -0.472 0.536 -0.880 0.379 0.624 0.218
## priorepilepsy 0.312 0.424 0.737 0.461 1.366 0.595
## status -0.317 0.584 -0.543 0.587 0.728 0.232
## ageyears -0.048 0.034 -1.412 0.158 0.953 0.892
## SEXnumeric 0.819 0.454 1.802 0.072 2.267 0.931
## upper .95
## intercept 90.459
## arm 2.860
## TYPESTATUSnumeric 2.891
## day 3.888
## earlyacademicyear 3.411
## white 4.267
## structuraletiology 1.783
## priorepilepsy 3.136
## status 2.287
## ageyears 1.019
## SEXnumeric 5.523