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#' @export
tdSim.method2 <-function(N, duration, lambda12, lambda23=NULL, lambda13, HR=NULL,
exp.prop,rateC, min.futime = 0, min.postexp.futime = 0){
try(if(is.null(lambda23) & is.null(HR)){stop("either lambda23 or HR(Hazard ratio) must be set")})
if(is.null(lambda23) & !is.null(HR)){
lambda23 = lambda13 * HR
}
expose<-rbinom(n=N,size=1,prob=exp.prop)
t12 <- rep(NA, N)
t13 <- rep(NA, N)
t23 <- rep(NA, N)
t12[as.logical(expose)] <- rexp(n=sum(expose), rate=lambda12)
t13[as.logical(expose)] <- Inf
t23[as.logical(expose)] <- rexp(n=sum(expose), rate=lambda23)
t12[!as.logical(expose)] <- Inf
t13[!as.logical(expose)] <- rexp(n=N-sum(expose), rate=lambda13)
t23[!as.logical(expose)] <- Inf
# C <- runif(n=N, min=0, max=1000)
C <- rexp(n=N, rate=rateC)
C=pmin(C,rep(duration,length(C)))
df <- data.frame(matrix(ncol = 5, nrow = N))
df[,1] = seq(N)
index <- C < pmin(t12,t13)
if(sum(index) > 0){
df[index, c(2,3)] <- data.frame(C[index],C[index])
df[index, c(4,5)] <- cbind(rep(0,sum(index)),rep(0,sum(index)))}
index <- t13 <= pmin(t12, C)
if(sum(index) > 0){
df[index, c(2,3)] <- data.frame(t13[index], t13[index])
df[index, c(4,5)] <- cbind(rep(0,sum(index)),rep(1,sum(index)))}
index <- (t12 <= pmin(t13, C) & (t12+t23) > C)
if(sum(index) > 0){
df[index, c(2,3)] <- data.frame(t12[index], C[index])
df[index, c(4,5)] <- cbind(rep(1,sum(index)),rep(0,sum(index)))}
index <- (t12 < t13 & (t12+t23) <= C)
if(sum(index) > 0){
df[index, c(2,3)] <- data.frame(t12[index], t12[index]+t23[index])
df[index, c(4,5)] <- cbind(rep(1,sum(index)),rep(1,sum(index)))}
colnames(df) = c('id',"exp.time",'end','exp','status')
if(min.futime>0){
df <- df[df$end>min.futime,]
df$id <- seq(nrow(df))
}
df_exp <- df[df$exp==1, ]
if(min.postexp.futime>0){
if(sum(df_exp$end-df_exp$exp.time > min.postexp.futime) == 0){
print('no exposure left')
}
df_exp <- df_exp[df_exp$end-df_exp$exp.time > min.postexp.futime,]
}
df1 <- df_exp
df1$start <- 0
df1$stop <- df1$exp.time
df1$status <- 0
df1$x <- 0
df2 <- df_exp
df2$start <- df2$exp.time
df2$stop <- df2$end
df2$status <- df2$status
df2$x <- 1
df3 <- df[df$exp==0, ]
df3$start <- 0
df3$stop <- df3$end
df3$status <- df3$status
df3$x <- 0
merge_exposed <- merge(df1[,c("id","start","stop","status","x")],df2[,c("id","start","stop","status","x")],all.x=TRUE,all.y=TRUE)
merged_df <- merge(merge_exposed, df3[,c("id","start","stop","status","x")],all.x=TRUE,all.y=TRUE)
return(merged_df)
}
#' @export
getpower.method2=function(nSim=500, N, duration=24, scenario,lambda12, lambda23=NULL, lambda13, HR=NULL,exp.prop,rateC,
min.futime, min.postexp.futime,output.fn, simu.plot=FALSE)
{ set.seed(999)
try(if(is.null(lambda23) & is.null(HR)){stop("either lambda23 or HR(Hazard ratio) must be set")})
if(is.null(lambda23) & !is.null(HR)){
lambda23 = lambda13 * HR
}
#N=400;duration=24;medTTEC=24;rho=1;medTTC=14;b=0.3;er=0.2;s1=1;s2=6;fA=4;fB=4
res=matrix(0,nSim,10)
colnames(res)=c("N.eff","N.effexp.p","betahat","HR","signif","events",
"events_c","events_exp","medsurvt_c","medsurvt_exp")
alpha=.05
if(simu.plot){
dat <- tdSim.method2(N, duration, lambda12=lambda12, lambda23=lambda23, lambda13=lambda13, exp.prop=exp.prop,rateC=rateC, min.futime=min.futime, min.postexp.futime=min.postexp.futime)
plot_simuData(dat)
}
for(k in 1:nSim)
{
dat <- tdSim.method2(N, duration, lambda12=lambda12, lambda23=lambda23, lambda13=lambda13, exp.prop=exp.prop,rateC=rateC, min.futime=min.futime, min.postexp.futime=min.postexp.futime)
fit <- coxph(Surv(start,stop, status) ~ factor(x), data=dat)
sfit <- survfit(Surv(start,stop, status) ~ factor(x), data=dat)
res[k,"N.eff"] <- length(unique(dat$id))
res[k,"N.effexp.p"] <- sum(dat$x)/length(unique(dat$id))
res[k,"betahat"] <- summary(fit)$coef[,"coef"]
res[k,"HR"] <- summary(fit)$coef[,"exp(coef)"]
res[k,"signif"] <- ifelse(summary(fit)$coef[,"Pr(>|z|)"]<alpha,1,0)
res[k,"events"] <- sum(dat$status)
res[k,"events_c"] <- summary(sfit)$table[1,'events']
res[k,"events_exp"] <- summary(sfit)$table[2,'events']
res[k,"medsurvt_c"] <- summary(sfit)$table[1,'median']
res[k,"medsurvt_exp"] <- summary(sfit)$table[2,'median']
}
df=data.frame(i_scenario=scenario,
i_N=N,
i_min.futime=min.futime,
i_min.postexp.futime=min.postexp.futime,
i_exp.prop=exp.prop,
i_lambda12=lambda12,
i_lambda23=lambda23,
i_lambda13=lambda13,
i_rateC=rateC,
N_eff=mean(res[,"N.eff"]),
N_effexp_p=mean(res[,"N.effexp.p"]),
bhat=mean(res[,"betahat"]),
HR=mean(res[,"HR"]),
d=mean(res[,"events"]),
d_c=mean(res[,"events_c"]),
d_exp=mean(res[,"events_exp"]),
mst_c=mean(na.omit(res[,"medsurvt_c"])),
mst_exp=mean(na.omit(res[,"medsurvt_exp"])),
pow=mean(res[,"signif"])
)
if(file.exists(output.fn)){
write.table(df,file=output.fn,row.names=FALSE,col.names=FALSE,append=TRUE,sep=",")
}
else{
write.table(df,file=output.fn,row.names=FALSE,col.names=TRUE,sep=",")
}
return(df)
}
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