# NOT RUN {
arm <- rep(c("C3","T1","T2"), each=250)
schedule <- rbinom(length(arm), 1, 0.01)
entry <- rpois(length(arm), lambda=60)
entry <- entry - min(entry)
last_visit_dt <- entry + runif(length(arm), min=0, max=80)
event <- rbinom(length(arm), 1, 0.01)
dropout <- rbinom(length(arm), 1, 0.02)
dropout[event==1] <- 0
exit <- rep(NA, length(arm))
exit[event==1] <- last_visit_dt[event==1] + 5
exit[dropout==1] <- last_visit_dt[dropout==1] + 5
followup <- ifelse(event==1 | dropout==1, 0, 1)
interimData <- data.frame(arm=arm, schedule2=schedule, entry=entry, exit=exit,
last_visit_dt=last_visit_dt, event=event, dropout=dropout, complete=0,
followup=followup)
weights <- c(0.2, 0.4, 0.6)
for (j in 1:length(weights)){
completeTrial.pooledArms(interimData=interimData, nTrials=50, N=1500, enrollRatePeriod=24,
eventPriorWeight=weights[j], eventPriorRate=0.06, fuTime=80, visitSchedule=seq(0, 80, by=4),
visitSchedule2=c(0,seq(from=8,to=80,by=12)), saveDir="./", randomSeed=9)
}
pdf(file=paste0("./","rcdf_pooled_eventPriorRate=",0.06,".pdf"), width=6, height=5)
plotRCDF.pooledArms(target=c(60,30), power.axis=FALSE, eventPriorRate=0.06,
eventPriorWeight=weights, fileDir="./")
dev.off()
# }
Run the code above in your browser using DataLab