Nothing
### Clustering
simulWeib.clst<-function(N,duration,lambda,rho,beta,rateC,df,min.futime)
{
if(sum(df$cat_prop)!=1){
cat("Error: proportions of patients do not sum to 1")
}
else if(N<nrow(df)){
cat("Error: not enough number of patients")
}
else{
expose<-NULL
time<-NULL
status<-NULL
pats_grp<-rmultinom(n=1,size=N,prob=df$cat_prop)
clst_id<-unlist(mapply(rep,df$cat_id,pats_grp))
start<-rep(0,N)
for(i in 1:length(pats_grp)){
expose_i<-rbinom(n=sum(clst_id==df$cat_id[i]),size=1,prob=df$cat_exp.prop[i])
v_i<-runif(n=sum(clst_id==df$cat_id[i]))
Tlat<-(-log(v_i)/(lambda*exp(expose_i*beta)))^(1/rho)
C<-rexp(n=sum(clst_id==df$cat_id[i]),rate=rateC)
C<-pmin(C,rep(duration,length(C)))
time_i<-pmin(Tlat,C)
status_i<-as.numeric(Tlat<=C)
expose<-c(expose,expose_i)
time<-c(time,time_i)
status<-c(status,status_i)
}
if(min.futime==0){
return(data.frame(id=1:length(time),start=start,stop=time,status=status,x=expose,
clst_id=clst_id))
}
else{
return(data.frame(id=1:length(time),start=start,stop=time,status=status,x=expose,
clst_id=clst_id)[which(time>min.futime),])
}
}
}
# modified version to generate time-dependent dataset with clustering
#' @export
tdSim.clst<-function(N,duration=24,lambda,rho=1,beta,rateC,df,
prop.fullexp=0,maxrelexptime=1,min.futime=0,min.postexp.futime=0){
data<-simulWeib.clst(N,duration,lambda,rho,beta,rateC,df,min.futime)
if(sum(df$cat_prop)==1 & N>=nrow(df)){
if(prop.fullexp==0){
data_tdexposed<-data[data$x==1,]
}
else{
id_tdexposed<-sample(x = data[data$x==1,]$id,size = round(nrow(data[data$x==1,])*(1-prop.fullexp)))
data_tdexposed<-data[data$id %in% id_tdexposed,]
}
data_tdexposed$t_exposed<-runif(nrow(data_tdexposed),0,data_tdexposed$stop*maxrelexptime)
if(min.postexp.futime>0){
if(sum(data_tdexposed$stop-data_tdexposed$t_exposed>min.postexp.futime) == 0){
cat("Warning: no exposure left")
}
data_tdexposed<-data_tdexposed[data_tdexposed$stop-data_tdexposed$t_exposed>min.postexp.futime,]
}
new_data1<-data_tdexposed
new_data2<-data_tdexposed
new_data1$id<-data_tdexposed$id
new_data1$start<-data_tdexposed$start
new_data1$stop<-data_tdexposed$t_exposed
new_data1$status<-0
new_data1$x<-0
new_data1$clst_id<-data_tdexposed$clst_id
new_data2$id<-data_tdexposed$id
new_data2$start<-data_tdexposed$t_exposed
new_data2$stop<-data_tdexposed$stop
new_data2$status<-data_tdexposed$status
new_data2$x<-1
new_data2$clst_id<-data_tdexposed$clst_id
merged_tdexposed<-subset(na.omit(merge(new_data1,new_data2,all.x=TRUE,all.y=TRUE)))
merged_tdexposed$t_exposed<-NULL
full_data<-merge(merged_tdexposed,data[data$x==0,],all.x=TRUE,all.y=TRUE)
return(full_data)
}
}
# get.power function for clustering scenario
#' @export
getpower.clst<-function(nSim,N,duration=24,med.TTE.Control=24,rho=1,beta,med.TimeToCensor=14,df,type,scenario,
prop.fullexp=0,maxrelexptime=1,min.futime=0,min.postexp.futime=0,output.fn,simu.plot=FALSE)
{
lambda<-log(2)/med.TTE.Control
rateC=log(2)/med.TimeToCensor
#numsim=500
res=matrix(0,nSim,8)
colnames(res)=c("betahat","HR","signif","events",
"events_c","events_exp","medsurvt_c","medsurvt_exp")
N.eff<-matrix(0,nSim,length(df$cat_id))
N.effexp.p<-matrix(0,nSim,length(df$cat_id))
alpha=.05
if(simu.plot){
set.seed(999)
if(type == "fixed"){
dat <- simulWeib.clst(N=N,duration=duration,lambda=lambda,rho=rho,beta=beta,rateC=rateC,
df=df,min.futime=min.futime)
}
else{
dat <- tdSim.clst(N=N,duration=duration,lambda=lambda,rho=rho,beta=beta,rateC=rateC,
df=df,prop.fullexp=prop.fullexp,maxrelexptime=maxrelexptime,
min.futime=min.futime,min.postexp.futime=min.postexp.futime)
}
plot_simuData(dat)
}
set.seed(999)
for(k in 1:nSim)
{
if(type == "fixed"){
dat<-simulWeib.clst(N=N,duration=duration,lambda=lambda,rho=rho,beta=beta,rateC=rateC,
df=df,min.futime=min.futime)
}
else{
dat<-tdSim.clst(N=N,duration=duration,lambda=lambda,rho=rho,beta=beta,rateC=rateC,
df=df,prop.fullexp=prop.fullexp,maxrelexptime=maxrelexptime,
min.futime=min.futime,min.postexp.futime=min.postexp.futime)
}
fit <- coxph(Surv(start,stop, status) ~ factor(x)+cluster(clst_id), data=dat)
sfit <- survfit(Surv(start,stop, status) ~ factor(x)+cluster(clst_id), data=dat)
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']
for(j in 1:length(unique(dat$clst_id))){
N.eff[k,j]<-length(unique(dat[dat$clst_id==df$cat_id[j],]$id))
N.effexp.p[k,j]<-sum(dat[dat$clst_id==df$cat_id[j],]$x)/length(unique(dat[dat$clst_id==df$cat_id[j],]$id))
}
}
df=data.frame(i_scenario=scenario,
i_type=type,
i_N=N,
i_min.futime=min.futime,
i_min.postexp.futime=min.postexp.futime,
i_cat=df$cat_id,
i_cat_prop=df$cat_prop,
i_cat_exp.prop=df$cat_exp.prop,
i_exp.prop=sum(df$cat_prop*df$cat_exp.prop),
i_lambda=lambda,
i_rho=rho,
i_rateC=rateC,
i_beta=beta,
N_eff=colMeans(N.eff),
N_effexp_p=colMeans(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)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.