library(MedicalRiskPredictionModels) prepareExamples()
# Chunk1 library(Publish) library(data.table) data(Diabetes) Diabetes[1:5,c("weight","height")]
# Chunk2 Diabetes$height.m <- Diabetes$height*0.0254 Diabetes$weight.kg <- Diabetes$weight*0.4535929 Diabetes$bmi <- Diabetes$weight.kg/Diabetes$height.m^2 Diabetes$BMI <- cut(Diabetes$bmi,c(0,18,25,30,Inf),labels=c("UnderWeight","NormalWeight","OverWeight","Obese")) Diabetes[,c("weight","height","bmi","BMI")]
# Chunk3 library(data.table) setDT(long) long <- long[,list("psa.time"=psadate-psadate[1],psadate,psa),by=subject] long <- long[psa.time<=(2*365.25),] # psa doubling time formula psadt <- function(time,value){ # input date and psa value (log(2)/coef(lm(log(value)~time))[2])/365.25 # lm is linear model } # now apply function to individual subjects long[,list("psa.doublingtime"=psadt(psa.time,psa)),by=subject]
# Chunk4 (no competing risks) library(data.table) d$af.date <- as.Date(d$af.date) d$death.date <- as.Date(d$death.date) d$lost.date <- as.Date(d$lost.date) d$time <- pmin( # parallel minimum d$death.date, # event d$lost.date, # lost to follow up as.Date("2015-01-01") # administrative censoring ,na.rm=TRUE)-d$af.date # date of subject specific time origin d$event <- 0 # initialize all subjects d[!is.na(d$death.date),]$event <- 1 # event d
# Chunk5 (with competing risks) library(data.table) d$af.date <- as.Date(d$af.date) d$stroke.date <- as.Date(d$stroke.date) d$death.date <- as.Date(d$death.date) d$lost.date <- as.Date(d$lost.date) d$time <- pmin( # parallel minimum d$stroke.date, # event d$death.date, # competing risk d$lost.date, # lost to follow up as.Date("2015-01-01") # administrative censoring ,na.rm=TRUE) -d$af.date # date of subject specific time origin d$event <- 0 # initialize all subjects d[!is.na(d$stroke.date),]$event <- 1 # event d[!is.na(d$death.date) & is.na(d$stroke.date),]$event <- 2 # competing d
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