R/SAS FUNCTION TWO DIRECTION.R
In AshZhong/Goahead:
Defines functions
Fix_SS_SAS_Survival_Prob
SAS_Survival_Cutoff
findSAScutoff
library(shiny)
findSAScutoff<-function(n=50,CT=0.2,rate=0.8,direction='Greater'){
lambdat=-log(CT)
var=(lambdat)^2*CT^2/n
sd=sqrt(var)
if(direction=='Greater'){
p=CT+qnorm(rate)*sd
}
if(direction=='Less'){
p=CT-qnorm(rate)*sd
}
return(p)
}
SAS_Survival_Cutoff<-function(npatients=126,
a=1,b=1,
CT1.go=0.25,
false.go.CT1=TRUE,FGR.CT1=0.25,
CT1.nogo=0.25,
false.nogo.CT1=TRUE,FNGR.CT1=0.25,
CT2.go=0.3,
false.go.CT2=TRUE, FGR.CT2=0.5,
CT2.nogo=0.3,
false.nogo.CT2=TRUE, FNGR.CT2=0.5,
method='Frequentist',direction='Greater',
seed.num=369,para.exp=TRUE,
logic.go='and',logic.nogo='or'){
# n=ifelse(eventinput,nevents,npatients*maturity)
n = npatients
est1.go=NA
est2.go=NA
est1.nogo=NA
est2.nogo=NA
flag=rep(0,4)
overlap.flag=0
if(is.na(CT1.go)){
false.go.CT1=FALSE
}
if(is.na(CT1.nogo)){
false.nogo.CT1=FALSE
}
if(is.na(CT2.go)){
false.go.CT2=FALSE
}
if(is.na(CT2.nogo)){
false.nogo.CT2=FALSE
}
if(para.exp==TRUE&method=='Frequentist'){
if(direction=='Greater'){
if(false.go.CT1==TRUE){
est1.go<-findSAScutoff(n=n,CT=CT1.go,rate=1-FGR.CT1,direction='Greater')
}
if(false.go.CT2==TRUE){
est2.go<-findSAScutoff(n=n,CT=CT2.go,rate=1-FGR.CT2,direction='Greater')
}
if(false.nogo.CT1==TRUE){
est1.nogo<-findSAScutoff(n=n,CT=CT1.nogo,rate=1-FNGR.CT1,direction='Less')
}
if(false.nogo.CT2==TRUE){
est2.nogo<-findSAScutoff(n=n,CT=CT2.nogo,rate=1-FNGR.CT2,direction='Less')
}
if(any(is.na(c(est1.go,est2.go)))){logic.go='and'}
if(any(is.na(c(est1.nogo,est2.nogo)))){logic.nogo='and'}
if(logic.go=='and'){
go_cutoff=max(est1.go,est2.go,na.rm=TRUE)
}
if(logic.go=='or'){
go_cutoff=min(est1.go,est2.go,na.rm=TRUE)
}
if(logic.nogo=='and')
{
nogo_cutoff=min(est1.nogo,est2.nogo,na.rm=TRUE)
}
if(logic.nogo=='or')
{
nogo_cutoff=max(est1.nogo,est2.nogo,na.rm=TRUE)
}
if(go_cutoff>=nogo_cutoff){return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
else{
overlap.flag=1
return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
if(all(flag==0)==FALSE){return(list(cutoff=c(NA,NA),flag=flag,overlap=overlap.flag))}
}
if(direction=='Less'){
if(false.go.CT1==TRUE){
est1.go<-findSAScutoff(n=n,CT=CT1.go,rate=1-FGR.CT1,direction='Less')
}
if(false.go.CT2==TRUE){
est2.go<-findSAScutoff(n=n,CT=CT2.go,rate=1-FGR.CT2,direction='Less')
}
if(false.nogo.CT1==TRUE){
est1.nogo<-findSAScutoff(n=n,CT=CT1.nogo,rate=1-FNGR.CT1,direction='Greater')
}
if(false.nogo.CT2==TRUE){
est2.nogo<-findSAScutoff(n=n,CT=CT2.nogo,rate=1-FNGR.CT2,direction='Greater')
}
if(any(is.na(c(est1.go,est2.go)))){logic.go='and'}
if(any(is.na(c(est1.nogo,est2.nogo)))){logic.nogo='and'}
if(logic.go=='and'){
go_cutoff=min(est1.go,est2.go,na.rm=TRUE)
}
if(logic.go=='or'){
go_cutoff=max(est1.go,est2.go,na.rm=TRUE)
}
if(logic.nogo=='and')
{
nogo_cutoff=max(est1.nogo,est2.nogo,na.rm=TRUE)
}
if(logic.nogo=='or')
{
nogo_cutoff=min(est1.nogo,est2.nogo,na.rm=TRUE)
}
if(go_cutoff<=nogo_cutoff){return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
else{
overlap.flag=1
return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
if(all(flag==0)==FALSE){return(list(cutoff=c(NA,NA),flag=flag,overlap=overlap.flag))}
}
}
if(para.exp==FALSE){
# The same as SAB
temp<-SAB_Bin_Cutoff(n=n,a=a,b=b,
CT1.go=CT1.go,
false.go.CT1=false.go.CT1,FGR.CT1=FGR.CT1,
CT1.nogo=CT1.nogo,
false.nogo.CT1=false.nogo.CT1,FNGR.CT1=FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2=false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2=false.nogo.CT2, FNGR.CT2=FNGR.CT2,
method=method,direction=direction,
logic.go=logic.go,logic.nogo=logic.nogo)
return(list(cutoff=temp$cutoff/n,flag=temp$flag,overlap=temp$overlap))
# if(method=='Bayesian'){
# temp<-SAB_Bin_Cutoff(n=n,a=a,b=b,
# CT1.go=CT1.go,
# false.go.CT1=false.go.CT1,FGR.CT1=FGR.CT1,
# CT1.nogo=CT1.nogo,
# false.nogo.CT1=false.nogo.CT1,FNGR.CT1=FNGR.CT1,
# CT2.go=CT2.go,
# false.go.CT2=false.go.CT2, FGR.CT2=FGR.CT2,
# CT2.nogo=CT2.nogo,
# false.nogo.CT2=false.nogo.CT2, FNGR.CT2=FNGR.CT2,
# method=method,direction=direction,
# logic.go=logic.go,logic.nogo=logic.nogo)
# return(list(cutoff=temp$cutoff/n,flag=temp$flag,overlap=temp$overlap))
# }
# if(method=='Frequentist'){
# if(direction=='Greater'){
#
# if(false.go.CT1==TRUE){
# sd.n1=sqrt(CT1.go*(1-CT1.go)/n)
# est1.go<-qnorm(1-FGR.CT1,mean=CT1.go,sd=sd.n1)
# }
# if(false.go.CT2==TRUE){
# sd.n2=sqrt(CT2.go*(1-CT2.go)/n)
# est2.go<-qnorm(1-FGR.CT2,mean=CT2.go,sd=sd.n2)
# }
# if(false.nogo.CT1==TRUE){
# sd.n1=sqrt(CT1.nogo*(1-CT1.nogo)/n)
# est1.nogo<-qnorm(FNGR.CT1,mean=CT1.nogo,sd=sd.n1)
# }
# if(false.nogo.CT2==TRUE){
# sd.n2=sqrt(CT2.nogo*(1-CT2.nogo)/n)
# est2.nogo<-qnorm(FNGR.CT2,mean=CT2.nogo,sd=sd.n2)
# }
#
#
# if(any(is.na(c(est1.go,est2.go)))){logic.go='and'}
# if(any(is.na(c(est1.nogo,est2.nogo)))){logic.nogo='and'}
# if(logic.go=='and'){
# go_cutoff=max(est1.go,est2.go,na.rm=TRUE)
# }
# if(logic.go=='or'){
# go_cutoff=min(est1.go,est2.go,na.rm=TRUE)
# }
# if(logic.nogo=='and')
# {
# nogo_cutoff=min(est1.nogo,est2.nogo,na.rm=TRUE)
# }
# if(logic.nogo=='or')
# {
# nogo_cutoff=max(est1.nogo,est2.nogo,na.rm=TRUE)
# }
#
#
# if(go_cutoff>=nogo_cutoff){return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
# else{
# overlap.flag=1
# return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
# if(all(flag==0)==FALSE){return(list(cutoff=c(NA,NA),flag=flag,overlap=overlap.flag))}
#
# }
# if(direction=='Less'){
# if(false.go.CT1==TRUE){
# sd.n1=sqrt(CT1.go*(1-CT1.go)/n)
# est1.go<-qnorm(FGR.CT1,mean=CT1.go,sd=sd.n1)
# }
# if(false.go.CT2==TRUE){
# sd.n2=sqrt(CT2.go*(1-CT2.go)/n)
# est2.go<-qnorm(FGR.CT2,mean=CT2.go,sd=sd.n2)
# }
# if(false.nogo.CT1==TRUE){
# sd.n1=sqrt(CT1.nogo*(1-CT1.nogo)/n)
# est1.nogo<-qnorm(1-FNGR.CT1,mean=CT1.nogo,sd=sd.n1)
# }
# if(false.nogo.CT2==TRUE){
# sd.n2=sqrt(CT2.nogo*(1-CT2.nogo)/n)
# est2.nogo<-qnorm(1-FNGR.CT2,mean=CT2.nogo,sd=sd.n2)
# }
#
# if(any(is.na(c(est1.go,est2.go)))){logic.go='and'}
# if(any(is.na(c(est1.nogo,est2.nogo)))){logic.nogo='and'}
# if(logic.go=='and'){
# go_cutoff=min(est1.go,est2.go,na.rm=TRUE)
# }
# if(logic.go=='or'){
# go_cutoff=max(est1.go,est2.go,na.rm=TRUE)
# }
# if(logic.nogo=='and')
# {
# nogo_cutoff=max(est1.nogo,est2.nogo,na.rm=TRUE)
# }
# if(logic.nogo=='or')
# {
# nogo_cutoff=min(est1.nogo,est2.nogo,na.rm=TRUE)
# }
#
# if(go_cutoff<=nogo_cutoff){return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
# else{
# overlap.flag=1
# return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
# if(all(flag==0)==FALSE){return(list(cutoff=c(NA,NA),flag=flag,overlap=overlap.flag))}
# }
#
# }
}
}
####################################################################
Fix_SS_SAS_Survival_Prob<-function(npatients=126,
a=1,b=1,
mean=c(0.15,0.9),
CT1.go=0.25,
false.go.CT1=TRUE,FGR.CT1=0.25,
CT1.nogo=0.25,
false.nogo.CT1=TRUE,FNGR.CT1=0.25,
CT2.go=0.3,
false.go.CT2=TRUE, FGR.CT2=0.5,
CT2.nogo=0.3,
false.nogo.CT2=TRUE, FNGR.CT2=0.5,
overlap.option='GO',plot.figure=TRUE,
method='Bayesian',direction='Greater',
seed.num=369,para.exp=TRUE,
logic.go='and',logic.nogo='or'){
# n=ifelse(eventinput,nevents,npatients*maturity)
n=npatients
minmean=min(mean)
maxmean=max(mean)
meanseq=seq(minmean,maxmean,0.05)
go_prob<-rep(NA,length(mean))
nogo_prob<-rep(NA,length(mean))
inconclusive_prob<-rep(NA,length(mean))
go_prob_plot<-rep(NA,length(meanseq))
nogo_prob_plot<-rep(NA,length(meanseq))
inconclusive_prob_plot<-rep(NA,length(meanseq))
index=1
unsatisfied.flag=0
overlap.flag=0
if(para.exp==FALSE){
sd.n=sqrt(mean*(1-mean)/n)
sd.nseq=sqrt(meanseq*(1-meanseq)/n)
}
if(para.exp==TRUE){
lambdat=-log(mean)
lambdatseq=-log(meanseq)
var=(lambdat)^2*mean^2/n
varseq=(lambdatseq)^2*meanseq^2/n
sd.n=sqrt(var)
sd.nseq=sqrt(varseq)
}
temp=SAS_Survival_Cutoff(npatients=n,
a=a,b=b,
CT1.go=CT1.go,
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo = CT1.nogo,
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2 = false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method=method,direction=direction,
seed.num=seed.num,para.exp = para.exp,
logic.go = logic.go,logic.nogo = logic.nogo)
###SAS####
true_go_cutoff<-ifelse(temp$overlap==0,temp$cutoff[1],(overlap.option=='GO')*temp$cutoff[1]+(overlap.option=='NOGO')*(temp$cutoff[2]))
true_nogo_cutoff<-ifelse(temp$overlap==0,temp$cutoff[2],(overlap.option=='GO')*temp$cutoff[1]+(overlap.option=='NOGO')*(temp$cutoff[2]))
if (para.exp==FALSE){
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=1-pbinom(temp$cutoff[1]*n-1,n,mean)
nogo_prob=pbinom(temp$cutoff[2]*n-1,n,mean)
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=1-pbinom(temp$cutoff[1]*n-1,n,meanseq)
nogo_prob_plot=pbinom(temp$cutoff[2]*n-1,n,meanseq)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=1-pbinom(temp$cutoff[1]*n-1,n,mean)
nogo_prob=1-go_prob
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=1-pbinom(temp$cutoff[1]*n-1,n,meanseq)
nogo_prob_plot=1-go_prob_plot
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob=1-pbinom(temp$cutoff[2]*n-1,n,mean)
nogo_prob=1-go_prob
inconclusive_prob=1-go_prob-nogo_prob
nogo_prob_plot=pbinom(temp$cutoff[2]*n-1,n,meanseq)
go_prob_plot=1-nogo_prob_plot
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(mean))
go_prob=rep(NA,length(mean))
inconclusive_prob=rep(NA,length(mean))
}
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=pbinom(temp$cutoff[1]*n,n,mean)
nogo_prob=1-pbinom(temp$cutoff[2]*n,n,mean)
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=pbinom(temp$cutoff[1]*n,n,meanseq)
nogo_prob_plot=1-pbinom(temp$cutoff[2]*n,n,meanseq)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=pbinom(temp$cutoff[1]*n,n,mean)
nogo_prob=1-go_prob
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=pbinom(temp$cutoff[1]*n,n,meanseq)
nogo_prob_plot=1-go_prob_plot
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob=pbinom(temp$cutoff[2]*n,n,mean)
nogo_prob=1-go_prob
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=pbinom(temp$cutoff[2]*n,n,meanseq)
nogo_prob_plot=1-go_prob_plot
inconclusive_prob_plot=1-go_prob-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(mean))
go_prob=rep(NA,length(mean))
inconclusive_prob=rep(NA,length(mean))
}
}
}
if (para.exp==TRUE){
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=1-pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
nogo_prob=pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
go_prob_plot=1-pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[1]>=1)){
go_prob[temp$cutoff[1]>=1]=0
go_prob_plot[temp$cutoff[1]>=1]=0
}
if(any(temp$cutoff[1]<=0)){
go_prob[temp$cutoff[1]<=0]=1
go_prob_plot[temp$cutoff[1]<=0]=1
}
if(any(temp$cutoff[2]<=0)){
nogo_prob[temp$cutoff[2]<=0]=0
nogo_prob_plot[temp$cutoff[2]<=0]=0
}
if(any(temp$cutoff[2]>=1)){
nogo_prob[temp$cutoff[2]>=1]=1
nogo_prob_plot[temp$cutoff[2]>=1]=1
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=1-pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
nogo_prob=pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
go_prob_plot=1-pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[1]>=1)){
go_prob[temp$cutoff[1]>=1]=0
go_prob_plot[temp$cutoff[1]>=1]=0
}
if(any(temp$cutoff[1]<=0)){
go_prob[temp$cutoff[1]<=0]=1
go_prob_plot[temp$cutoff[1]<=0]=1
}
if(any(temp$cutoff[1]<=0)){
nogo_prob[temp$cutoff[1]<=0]=0
nogo_prob_plot[temp$cutoff[1]<=0]=0
}
if(any(temp$cutoff[1]>=1)){
nogo_prob[temp$cutoff[1]>=1]=1
nogo_prob_plot[temp$cutoff[1]>=1]=1
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob=1-pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
nogo_prob=pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
go_prob_plot=1-pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[2]>=1)){
go_prob[temp$cutoff[2]>=1]=0
go_prob_plot[temp$cutoff[2]>=1]=0
}
if(any(temp$cutoff[2]<=0)){
go_prob[temp$cutoff[2]<=0]=1
go_prob_plot[temp$cutoff[2]<=0]=1
}
if(any(temp$cutoff[2]<=0)){
nogo_prob[temp$cutoff[2]<=0]=0
nogo_prob_plot[temp$cutoff[2]<=0]=0
}
if(any(temp$cutoff[2]>=1)){
nogo_prob[temp$cutoff[2]>=1]=1
nogo_prob_plot[temp$cutoff[2]>=1]=1
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(mean))
go_prob=rep(NA,length(mean))
inconclusive_prob=rep(NA,length(mean))
}
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
nogo_prob=1-pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
go_prob_plot=pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=1-pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[1]>=1)){
go_prob[temp$cutoff[1]>=1]=1
go_prob_plot[temp$cutoff[1]>=1]=1
}
if(any(temp$cutoff[1]<=0)){
go_prob[temp$cutoff[1]<=0]=0
go_prob_plot[temp$cutoff[1]<=0]=0
}
if(any(temp$cutoff[2]<=0)){
nogo_prob[temp$cutoff[2]<=0]=1
nogo_prob_plot[temp$cutoff[2]<=0]=1
}
if(any(temp$cutoff[2]>=1)){
nogo_prob[temp$cutoff[2]>=1]=0
nogo_prob_plot[temp$cutoff[2]>=1]=0
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
nogo_prob=1-pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
go_prob_plot=pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=1-pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[1]>=1)){
go_prob[temp$cutoff[1]>=1]=1
go_prob_plot[temp$cutoff[1]>=1]=1
}
if(any(temp$cutoff[1]<=0)){
go_prob[temp$cutoff[1]<=0]=0
go_prob_plot[temp$cutoff[1]<=0]=0
}
if(any(temp$cutoff[1]<=0)){
nogo_prob[temp$cutoff[1]<=0]=1
nogo_prob_plot[temp$cutoff[1]<=0]=1
}
if(any(temp$cutoff[1]>=1)){
nogo_prob[temp$cutoff[1]>=1]=0
nogo_prob_plot[temp$cutoff[1]>=1]=0
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob=pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
nogo_prob=1-pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
go_prob_plot=pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=1-pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[2]>=1)){
go_prob[temp$cutoff[2]>=1]=1
go_prob_plot[temp$cutoff[2]>=1]=1
}
if(any(temp$cutoff[2]<=0)){
go_prob[temp$cutoff[2]<=0]=0
go_prob_plot[temp$cutoff[2]<=0]=0
}
if(any(temp$cutoff[2]<=0)){
nogo_prob[temp$cutoff[2]<=0]=1
nogo_prob_plot[temp$cutoff[2]<=0]=1
}
if(any(temp$cutoff[2]>=1)){
nogo_prob[temp$cutoff[2]>=1]=0
nogo_prob_plot[temp$cutoff[2]>=1]=0
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(mean))
go_prob=rep(NA,length(mean))
inconclusive_prob=rep(NA,length(mean))
}
}
}
########################################################################
if(plot.figure==TRUE){
delta=meanseq
p_nogo=nogo_prob_plot*100
p_grey=inconclusive_prob_plot*100
p_go=go_prob_plot*100
if(true_go_cutoff!=true_nogo_cutoff){
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
plot(delta,p_go,xlab=expression(paste("Survival probablity",sep="")),
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=range(delta),
ylim=c(0,100),type="n",axes=F)
axis(1, at=seq(minmean,maxmean,0.05), labels=T)
axis(2, at=seq(0,100,10),labels=T)
#text(rep(-0.6,6),seq(0,100,20),seq(0,100,20),adj=1,xpd=T)
box()
#abline(h=seq(0,100,10),lty=3,col=rgb(0.8,0.8,0.8),lwd=1)
points(delta,p_nogo,type="b",pch=16,col=rgb(0.9,0,0),lwd=3,lty=1)
points(delta,p_grey,type="b",pch=4,col=rgb(0.9,0.6,0),lwd=3,lty=6)
points(delta,p_go,type="b",pch=2,col=rgb(0,0.7,0),lwd=3,lty=2)
# if(eventinput==TRUE){
# text(min(mean),140,bquote(Number~of~events==~.(nevents)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
# }
# if(eventinput==FALSE){
# text(min(mean),140,bquote(Number~of~patients==~.(npatients)~';'~Maturity==~.(maturity)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
# }
text(min(mean),140,bquote(Number~of~patients==~.(npatients)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
if(overlap.flag==0&unsatisfied.flag==0){
if(direction=='Greater'){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
text(minmean,110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
if(direction=='Less'){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
text(minmean,110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=='GO'){
if(direction=="Greater"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(direction=="Less"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=='NOGO'){
if(direction=="Greater"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(direction=="Less"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
if(unsatisfied.flag==1){
text(minmean,130,paste0('Warning: We could not find classification of zones to satisfy your both GO and NOGO criterions at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(minmean,120,paste0('Please check and modify your desicion rule or the range of sample size!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
}
#######################################################################
if(plot.figure==TRUE){
delta=meanseq
p_nogo=nogo_prob_plot*100
p_grey=inconclusive_prob_plot*100
p_go=go_prob_plot*100
if(true_go_cutoff==true_nogo_cutoff){
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
plot(delta,p_go,xlab=expression(paste("Survival probablity",sep="")),
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=range(delta),
ylim=c(0,100),type="n",axes=F)
axis(1, at=seq(minmean,maxmean,0.05), labels=T)
axis(2, at=seq(0,100,10),labels=T)
#text(rep(-0.6,6),seq(0,100,20),seq(0,100,20),adj=1,xpd=T)
box()
#abline(h=seq(0,100,10),lty=3,col=rgb(0.8,0.8,0.8),lwd=1)
points(delta,p_nogo,type="b",pch=16,col=rgb(0.9,0,0),lwd=3,lty=1)
#points(delta,p_grey,type="b",pch=4,col=rgb(0.9,0.6,0),lwd=3,lty=6)
points(delta,p_go,type="b",pch=2,col=rgb(0,0.7,0),lwd=3,lty=2)
# if(eventinput==TRUE){
# text(min(mean),140,bquote(Number~of~events==~.(nevents)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
# }
# if(eventinput==FALSE){
# text(min(mean),140,bquote(Number~of~patients==~.(npatients)~';'~Maturity==~.(maturity)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
# }
text(min(mean),140,bquote(Number~of~patients==~.(npatients)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
if(overlap.flag==0&unsatisfied.flag==0){
if(direction=='Greater'){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
if(direction=='Less'){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=='GO'){
if(direction=="Greater"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(direction=="Less"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=='NOGO'){
if(direction=="Greater"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(direction=="Less"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
if(unsatisfied.flag==1){
text(minmean,130,paste0('Warning: We could not find classification of zones to satisfy your both GO and NOGO criterions at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(minmean,120,paste0('Please check and modify your desicion rule or the range of sample size!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
}
#######################################################################
return(list(go_prob=go_prob,nogo_prob=nogo_prob,
inconclusive_prob=inconclusive_prob,
overlap.flag=overlap.flag,overlap.option=overlap.option,
unsatisfied.flag=unsatisfied.flag,cutoff=temp$cutoff,true_cutoff=c(true_go_cutoff,true_nogo_cutoff))
)
}
Vary_SS_SAS_Survival_Prob<-function(npatientsmin=100,npatientsmax=500,
a=1/3,b=1,
mean=0.3,
CT1.go=0.25,
false.go.CT1=TRUE,FGR.CT1=0.25,
CT1.nogo=0.3,
false.nogo.CT1=TRUE,FNGR.CT1=0.25,
CT2.go=0.3,
false.go.CT2=TRUE, FGR.CT2=0.5,
CT2.nogo=0.3,
false.nogo.CT2=TRUE, FNGR.CT2=0.5,
overlap.option='GO',plot.cutoff=TRUE,plot.prob=TRUE,
method="Bayesian",direction="Greater",
seed.num=369,para.exp=TRUE,
logic.go='and',logic.nogo='or'){
# nmin=ifelse(eventinput,neventsmin,npatientsmin*maturity)
# nmax=ifelse(eventinput,neventsmax,npatientsmax*maturity)
nmin=npatientsmin
nmax=npatientsmax
nseq=unique(round(c(seq(nmin,(nmax+nmin)/2,length=6)[-6],seq((nmax+nmin)/2,nmax,length=6))))
go_prob<-matrix(NA,ncol=length(nseq),nrow=length(mean))
nogo_prob<-matrix(NA,ncol=length(nseq),nrow=length(mean))
inconclusive_prob<-matrix(NA,ncol=length(nseq),nrow=length(mean))
go_cutoff<-rep(NA,length(nseq))
nogo_cutoff<-rep(NA,length(nseq))
index=1
n_unsatisfied=NA
n_overlap=NA
if (para.exp==FALSE){
for(i in nseq){
temp=SAS_Survival_Cutoff(npatients=i,
a=a,b=b,
CT1.go=CT1.go,
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo = CT1.nogo,
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2 = false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method=method,direction=direction,
seed.num=seed.num,para.exp = para.exp,
logic.go=logic.go,logic.nogo=logic.nogo)
overlap = temp$overlap
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob[,index]=1-pbinom(temp$cutoff[1]*i-1,i,mean)
nogo_prob[,index]=pbinom(temp$cutoff[2]*i-1,i,mean)
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob[,index]=1-pbinom(temp$cutoff[1]*i-1,i,mean)
nogo_prob[,index]=pbinom(temp$cutoff[1]*i-1,i,mean)
inconclusive_prob[,index]=0
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob[,index]=1-pbinom(temp$cutoff[2]*i-1,i,mean)
nogo_prob[,index]=pbinom(temp$cutoff[2]*i-1,i,mean)
inconclusive_prob[,index]=0
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)==FALSE){
n_unsatisfied=c(n_unsatisfied,i)
}
index=index+1
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob[,index]=pbinom(temp$cutoff[1]*i,i,mean)
nogo_prob[,index]=1-pbinom(temp$cutoff[2]*i,i,mean)
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob[,index]=pbinom(temp$cutoff[1]*i,i,mean)
nogo_prob[,index]=1-pbinom(temp$cutoff[1]*i,i,mean)
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob[,index]=pbinom(temp$cutoff[2]*i,i,mean)
nogo_prob[,index]=1-pbinom(temp$cutoff[2]*i,i,mean)
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)==FALSE){
n_unsatisfied=c(n_unsatisfied,i)
}
index=index+1
}
}
}
if (para.exp==TRUE){
ncore <- detectCores()
cl<-makeCluster(ncore)
registerDoParallel(cl)
results<-foreach(i = nseq,.export=c('SAS_Survival_Cutoff',"SAB_Bin_Cutoff"),.combine=rbind) %dopar% {
temp=SAS_Survival_Cutoff(npatients=i,
a=a,b=b,
CT1.go=CT1.go,
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo = CT1.nogo,
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2 = false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method=method,direction=direction,
seed.num=seed.num,para.exp = para.exp,
logic.go=logic.go,logic.nogo=logic.nogo)
c(temp$overlap,temp$cutoff)
}
stopCluster(cl)
go_cutoff<-results[,2]
nogo_cutoff<-results[,3]
overlap<-results[,1]
for(mean.index in 1:length(mean)){
mean_temp=mean[mean.index]
sd.i=sqrt(mean_temp*(1-mean_temp)/nseq)
if(para.exp==FALSE){
sd.i=sqrt(mean_temp*(1-mean_temp)/nseq)
}
if(para.exp==TRUE){
lambdat=-log(mean_temp)
var=(lambdat)^2*mean_temp^2/nseq
sd.i=sqrt(var)
}
true_go_cutoff<-go_cutoff
true_nogo_cutoff<-nogo_cutoff
if(direction=='Greater'){
true_go_cutoff[overlap==1]<-(overlap.option=='GO')*go_cutoff[overlap==1]+(overlap.option=='NOGO')*nogo_cutoff[overlap==1]
true_nogo_cutoff[overlap==1]<-(overlap.option=='GO')*go_cutoff[overlap==1]+(overlap.option=='NOGO')*nogo_cutoff[overlap==1]
go_prob[mean.index,]=1-pnorm(true_go_cutoff,mean=mean_temp,sd=sd.i)
nogo_prob[mean.index,]=pnorm(true_nogo_cutoff,mean=mean_temp,sd=sd.i)
if(any(true_go_cutoff>=1)){
go_prob[mean.index,true_go_cutoff>=1]=0
}
if(any(true_go_cutoff<=0)){
go_prob[mean.index,cutoff<=0]=1
}
if(any(true_nogo_cutoff<=0)){
nogo_prob[mean.index,true_nogo_cutoff<=0]=0
}
if(any(true_nogo_cutoff>=1)){
nogo_prob[mean.index,true_nogo_cutoff>=1]=1
}
inconclusive_prob[mean.index,]=1-go_prob[mean.index,]-nogo_prob[mean.index,]
}
if(direction=='Less'){
true_go_cutoff[overlap==1]<-(overlap.option=='GO')*go_cutoff[overlap==1]+(overlap.option=='NOGO')*nogo_cutoff[overlap==1]
true_nogo_cutoff[overlap==1]<-(overlap.option=='GO')*go_cutoff[overlap==1]+(overlap.option=='NOGO')*nogo_cutoff[overlap==1]
go_prob[mean.index,]=pnorm(true_go_cutoff,mean=mean_temp,sd=sd.i)
nogo_prob[mean.index,]=1-pnorm(true_nogo_cutoff,mean=mean_temp,sd=sd.i)
if(any(true_go_cutoff>=1)){
go_prob[mean.index,true_go_cutoff>=1]=1
}
if(any(true_go_cutoff<=0)){
go_prob[mean.index,cutoff<=0]=0
}
if(any(true_nogo_cutoff<=0)){
nogo_prob[mean.index,true_nogo_cutoff<=0]=1
}
if(any(true_nogo_cutoff>=1)){
nogo_prob[mean.index,true_nogo_cutoff>=1]=0
}
inconclusive_prob[mean.index,]=1-go_prob[mean.index,]-nogo_prob[mean.index,]
}
}
}
n_overlap=n_overlap[-1]
n_unsatisfied=n_unsatisfied[-1]
####plot figure
if(plot.prob==TRUE){
for(j in 1:length(mean)){
p_go=go_prob[j,]*100
p_nogo=nogo_prob[j,]*100
class(p_nogo)
dim(p_nogo)
p_grey=100-p_go-p_nogo
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
cum_p_nogo=p_nogo
cum_p_grey=p_nogo+p_grey
cum_p_go=p_nogo+p_grey+p_go
delta=nseq
plot(delta,cum_p_go,xlab="Sample size",
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=c(nmin,nmax),
ylim=c(0,100),type="n",axes=F,pch=NA)
axis(1, at=nseq, labels=T)
axis(2, at=seq(0,100,10),labels=T)
#text(rep(-0.6,6),seq(0,100,20),seq(0,100,20),adj=1,xpd=T)
box()
#abline(h=seq(0,100,10),lty=3,col=rgb(0.8,0.8,0.8),lwd=1)
polygon(c(delta,rev(delta)),c(cum_p_nogo,rev(rep(0,length(delta)))),col=rgb(0.9,0,0),border=NA)
polygon(c(delta,rev(delta)),c(cum_p_grey,rev(cum_p_nogo)),col=rgb(0.9,0.6,0),border=NA)
polygon(c(delta,rev(delta)),c(cum_p_go,rev(cum_p_grey)),col=rgb(0,0.7,0),border=NA)
text((nmin+nmax)/2,120,paste0("True survival probability=",round(mean[j],3)),xpd=T,adj=0.5,cex=0.8)
if(any(overlap!=0)){
text(nmin,115,paste0('Warning: GO and NOGO cut-offs are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(nmin,110,paste(n_overlap),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#print(n_overlap)
text(nmin,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
}
if(plot.cutoff==TRUE){
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
ylim_max=max(c(go_cutoff,nogo_cutoff,round(CT1.go,3),round(CT2.go,3),round(CT1.nogo,3),round(CT2.nogo,3)),na.rm=TRUE)+0.1
ylim_min=min(c(go_cutoff,nogo_cutoff,round(CT1.go,3),round(CT2.go,3),round(CT1.nogo,3),round(CT2.nogo,3)),na.rm=TRUE)-0.1
plot(NA,NA,xlab='Sample size',ylab="Observed survival probability",xlim=c(max(0,range(nseq)[1]-10),range(nseq)[2]+10),ylim=c(ylim_min,ylim_max),type="n",axes=F,col=rgb(1,0,0),lty=1,lwd=2)
axis(1, at=nseq, labels=T)
axis(2, at=round(c(seq(ylim_min,ylim_max,round((ylim_max-ylim_min)/10,digits=2)),CT1.go,CT2.go,CT1.nogo,CT2.nogo),digits=2),labels=T)
#text(rep(-0.6,6),seq(0,100,20),seq(0,300,20),adj=1,xpd=T)
box()
lines(nseq,nogo_cutoff,col=rgb(0.9,0,0),lwd=2)
lines(nseq,go_cutoff,col=rgb(0,0.7,0),lwd=2)
legend('bottomright',legend=c("Cut off of GO","Cut off of NOGO"),
col=c(rgb(0,0.7,0),rgb(0.9,0,0)),
lwd=c(2,2),
lty=c(1,1),cex=0.5)
if(any(overlap!=0)){
text(nmin,ylim_max+abs(ylim_max-ylim_min)/10,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(nmin,110,paste(n_overlap),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#print(n_overlap)
text(nmin,ylim_max+abs(ylim_max-ylim_min)/20,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
return(list(overlap))
}
Interim_SAS<-function(num_interim=3,interim_n=c(50,100,150),
CT1.go=c(0.25,0.25,0.25),
false.go.CT1=c(TRUE,TRUE,TRUE),FGR.CT1=c(0.25,0.25,0.25),
CT1.nogo=c(0.25,0.25,0.25),
false.nogo.CT1=c(TRUE,TRUE,TRUE),FNGR.CT1=c(0.25,0.25,0.25),
CT2.go=c(0.25,0.25,0.25),
false.go.CT2=c(TRUE,TRUE,TRUE),FGR.CT2=c(0.25,0.25,0.25),
CT2.nogo=c(0.25,0.25,0.25),
false.nogo.CT2=c(TRUE,TRUE,TRUE),FNGR.CT2=c(0.25,0.25,0.25),
overlap.option=c('GO','GO','GO'),
method='Bayesian',direction=c("Greater",'Greater','Greater'),nsim_IA=10000,seed.num=369,
task=c('Futility','Superiority','Futility and superiority'),
logic.go=c('and','or','and'),
logic.nogo=c('and','or','or'),
a=1/3,b=1,mean=c(0.25),para.exp=TRUE){
# if(eventinput==FALSE){
# interim_n=floor(interim_n*maturity)
# }
interim_n=sort(interim_n)
go_cutoff<-rep(NA,num_interim)
nogo_cutoff<-rep(NA,num_interim)
true_go_cutoff<-rep(NA,num_interim)
true_nogo_cutoff<-rep(NA,num_interim)
overlap<-rep(NA,num_interim)
for(i in 1:num_interim){
temp<-SAS_Survival_Cutoff(npatients=interim_n[i],a=a,b=b,
CT1.go=CT1.go[i],
false.go.CT1=false.go.CT1[i],FGR.CT1=FGR.CT1[i],
CT1.nogo=CT1.nogo[i],
false.nogo.CT1=false.nogo.CT1[i],FNGR.CT1=FNGR.CT1[i],
CT2.go=CT2.go[i],
false.go.CT2=false.go.CT2[i], FGR.CT2=FGR.CT2[i],
CT2.nogo=CT2.nogo[i],
false.nogo.CT2=false.nogo.CT2[i], FNGR.CT2=FNGR.CT2[i],
method=method,direction=direction[i],
seed.num=seed.num,para.exp=para.exp,
logic.go=logic.go[i],logic.nogo=logic.nogo[i])
go_cutoff[i]<-temp$cutoff[1]
nogo_cutoff[i]<-temp$cutoff[2]
overlap[i]<-temp$overlap
###SAS####
true_go_cutoff[i]<-ifelse(overlap[i]==0,go_cutoff[i],(overlap.option[i]=='GO')*go_cutoff[i]+(overlap.option[i]=='NOGO')*(nogo_cutoff[i]))
true_nogo_cutoff[i]<-ifelse(overlap[i]==0,nogo_cutoff[i],(overlap.option[i]=='GO')*(go_cutoff[i])+(overlap.option[i]=='NOGO')*(nogo_cutoff[i]))
####
}
#####
temptable=c()
for(meanindex in 1:length(mean)){
set.seed(seed.num)
###SAS####
sim_data<-matrix(NA,nrow=nsim_IA,ncol=max(interim_n))
# if(para.exp==FALSE){
# sd=sqrt(mean[meanindex]*(1-mean[meanindex]))
# }
if(para.exp==FALSE){
for(k in 1:max(interim_n)){
sim_data[,k]<- rbinom(nsim_IA,1,mean[meanindex])
}
}
if(para.exp==TRUE){
lambdat=-log(mean[meanindex])
var=(lambdat)^2*mean[meanindex]^2
sd=sqrt(var)
for(k in 1:max(interim_n)){
sim_data[,k]<- rnorm(nsim_IA,mean=mean[meanindex],sd=sd)
}
}
cum_sim_data_temp<-apply(sim_data,1,cumsum)
cum_sim_data<-t(cum_sim_data_temp[interim_n,]/interim_n)
#####
go_matrix<-matrix(NA,nrow=nsim_IA,ncol=num_interim)
nogo_matrix<-matrix(NA,nrow=nsim_IA,ncol=num_interim)
inconclusive_matrix<-matrix(NA,nrow=nsim_IA,ncol=num_interim)
table<-matrix(NA,ncol=num_interim+1,nrow=10)
IA_go_matrix<-matrix(NA,nrow=nsim_IA,ncol=num_interim) ###whether continue to next stage
for(j in 1:num_interim ){
if(direction[j]=='Greater'){
go_matrix[,j]<-cum_sim_data[,j]>=true_go_cutoff[j]
nogo_matrix[,j]<-cum_sim_data[,j]<true_nogo_cutoff[j]
inconclusive_matrix[,j]<-rep(1,nsim_IA)-go_matrix[,j]-nogo_matrix[,j]
}
}
for(j in 1:num_interim ){
if(direction[j]=='Less'){
go_matrix[,j]<-cum_sim_data[,j]<=true_go_cutoff[j]
nogo_matrix[,j]<-cum_sim_data[,j]>true_nogo_cutoff[j]
inconclusive_matrix[,j]<-rep(1,nsim_IA)-go_matrix[,j]-nogo_matrix[,j]
}
}
for(ii in 1:(num_interim)){
if(task[ii]=='Futility'){
IA_go_matrix[,ii]=inconclusive_matrix[,ii]+go_matrix[,ii]
}
if(task[ii]=='Superiority'){
IA_go_matrix[,ii]=inconclusive_matrix[,ii]+nogo_matrix[,ii]
}
if(task[ii]=='Futility and superiority'){
IA_go_matrix[,ii]=inconclusive_matrix[,ii]
}
}
cum_IA_go_matrix<-t(apply(IA_go_matrix,1,cumprod))
for(j in 1:(num_interim)){
table[1,j]=interim_n[j]
table[2,j]=mean[meanindex]
table[3,j]=task[j]
if(j==1){
if(task[j]=='Superiority'|task[j]=='Futility and superiority'){
table[4,j]=round(sum(go_matrix[,j]==1)/nsim_IA,3)}else{table[4,j]=0}
if(task[j]=='Futility'|task[j]=='Futility and superiority'){
table[6,j]=round(sum(nogo_matrix[,j]==1)/nsim_IA,3)
}else{table[6,j]=0}
}else{
if(task[j]=='Superiority'|task[j]=='Futility and superiority'){
table[4,j]=round(sum(go_matrix[,j]==1&cum_IA_go_matrix[,j-1]==1)/nsim_IA,3)}else{table[4,j]=0}
if(task[j]=='Futility'|task[j]=='Futility and superiority'){
table[6,j]=round(sum(nogo_matrix[,j]==1&cum_IA_go_matrix[,j-1]==1)/nsim_IA,3)
}else{table[6,j]=0}
}
table[5,j]=round(sum(cum_IA_go_matrix[,j]==1)/nsim_IA,3)
if(task[j]=='Futility'){
table[7,j]=ifelse(direction[j]=='Greater',HTML(paste0('-/<',round(true_nogo_cutoff[j],3))),HTML(paste0('-/>',round(true_nogo_cutoff[j],3))))
}
if(task[j]=='Superiority'){
table[7,j]=ifelse(direction[j]=='Greater',HTML(paste0('>=',round(true_go_cutoff[j],3),'/-')),HTML(paste0('<=',round(true_go_cutoff[j],3),'/-')))
}
if(task[j]=='Futility and superiority'){
table[7,j]=ifelse(direction[j]=='Greater',HTML(paste0('>=',round(true_go_cutoff[j],3),' /','<',round(true_nogo_cutoff[j],3))),HTML(paste0('<=',round(true_go_cutoff[j],3),' / ','>',round(true_nogo_cutoff[j],3))))
}
table[10,j]=ifelse(direction[j]=='Greater',HTML(paste0('>=',round(go_cutoff[j],3))),HTML(paste0('<=',round(go_cutoff[j],3))))
table[9,j]=ifelse(direction[j]=='Greater',HTML(paste0('<',round(nogo_cutoff[j],3))),HTML(paste0('>',round(nogo_cutoff[j],3))))
table[8,j]<-ifelse(overlap[j]==1,paste0('GO/NOGO zones overlapped, classified by criterion of ',overlap.option[j]),'None')
}
expectss<-round(sum(as.numeric(table[1,1:num_interim])*(c(as.numeric(table[4,1:num_interim-1])+as.numeric(table[6,1:num_interim-1]),as.numeric(table[5,num_interim-1])))),3)
table[1,num_interim+1]=HTML(paste0(expectss,' (expected)'))
table[2,num_interim+1]=mean[meanindex]
table[3,num_interim+1]=''
table[4,num_interim+1]=round(sum(as.numeric(table[4,1:num_interim])),3)
table[5,num_interim+1]=round(as.numeric(table[5,num_interim]),3)
table[6,num_interim+1]=round(sum(as.numeric(table[6,1:num_interim])),3)
table[7,num_interim+1]=''
table[8,num_interim+1]=''
table[9,num_interim+1]=''
table[10,num_interim+1]=''
table<-as.table(table)
tablecolname<-c(paste0('Interim analysis ',1:(num_interim-1)),'Final analysis',"Summary")
tablerowname<-c('Sample size','True survival probability','Task','Success','To next interim/final or inconclusive',
'Stop','Superority/Futility zone','Warning',
'Cut off for NOGO rule',
'Cut off for GO rule')
table<-cbind(tablerowname,rep(meanindex,10),table)
colnames(table)<-c(" ",'Setting',tablecolname)
temptable=rbind(temptable,table)
}
return(temptable)
}
# #Interim_SAS(interim_n = c(66,131,197),CT1.go=c(0.5,0.9,0.7),FGR.CT1=c(1-0.71,0.653,0.0238),FNGR.CT1=c(0.707,1-0.653,1-0.0238),CT2.go=c(NA,NA,NA,NA),method='Frequentist',direction=c('Greater','Less','Greater'),task=c('Futility','Superiority','Superiority'),mean=c(0.9,1.8))
#
# Fix_SS_SAS_Survival_Prob(eventinput=TRUE,nevents=100,
# npatients=180,maturity=0.7,
# a=1,b=1,
# mean=c(0.15,0.9),
# CT1.go=0.38,
# false.go.CT1=TRUE,FGR.CT1=0.7,
# CT1.nogo=0.38,
# false.nogo.CT1=TRUE,FNGR.CT1=0.2,
# CT2.go=0.3,
# false.go.CT2=TRUE, FGR.CT2=0.5,
# CT2.nogo=0.3,
# false.nogo.CT2=TRUE, FNGR.CT2=0.5,
# overlap.option='GO',plot.figure=TRUE,
# method='Bayesian',direction='Greater',
# seed.num=369,para.exp=FALSE,
# logic.go='and',logic.nogo='or')
AshZhong/Goahead documentation built on May 5, 2022, 12:30 a.m.
R Package Documentation
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Defines functions Fix_SS_SAS_Survival_Prob SAS_Survival_Cutoff findSAScutoff
library(shiny)
findSAScutoff<-function(n=50,CT=0.2,rate=0.8,direction='Greater'){
lambdat=-log(CT)
var=(lambdat)^2*CT^2/n
sd=sqrt(var)
if(direction=='Greater'){
p=CT+qnorm(rate)*sd
}
if(direction=='Less'){
p=CT-qnorm(rate)*sd
}
return(p)
}
SAS_Survival_Cutoff<-function(npatients=126,
a=1,b=1,
CT1.go=0.25,
false.go.CT1=TRUE,FGR.CT1=0.25,
CT1.nogo=0.25,
false.nogo.CT1=TRUE,FNGR.CT1=0.25,
CT2.go=0.3,
false.go.CT2=TRUE, FGR.CT2=0.5,
CT2.nogo=0.3,
false.nogo.CT2=TRUE, FNGR.CT2=0.5,
method='Frequentist',direction='Greater',
seed.num=369,para.exp=TRUE,
logic.go='and',logic.nogo='or'){
# n=ifelse(eventinput,nevents,npatients*maturity)
n = npatients
est1.go=NA
est2.go=NA
est1.nogo=NA
est2.nogo=NA
flag=rep(0,4)
overlap.flag=0
if(is.na(CT1.go)){
false.go.CT1=FALSE
}
if(is.na(CT1.nogo)){
false.nogo.CT1=FALSE
}
if(is.na(CT2.go)){
false.go.CT2=FALSE
}
if(is.na(CT2.nogo)){
false.nogo.CT2=FALSE
}
if(para.exp==TRUE&method=='Frequentist'){
if(direction=='Greater'){
if(false.go.CT1==TRUE){
est1.go<-findSAScutoff(n=n,CT=CT1.go,rate=1-FGR.CT1,direction='Greater')
}
if(false.go.CT2==TRUE){
est2.go<-findSAScutoff(n=n,CT=CT2.go,rate=1-FGR.CT2,direction='Greater')
}
if(false.nogo.CT1==TRUE){
est1.nogo<-findSAScutoff(n=n,CT=CT1.nogo,rate=1-FNGR.CT1,direction='Less')
}
if(false.nogo.CT2==TRUE){
est2.nogo<-findSAScutoff(n=n,CT=CT2.nogo,rate=1-FNGR.CT2,direction='Less')
}
if(any(is.na(c(est1.go,est2.go)))){logic.go='and'}
if(any(is.na(c(est1.nogo,est2.nogo)))){logic.nogo='and'}
if(logic.go=='and'){
go_cutoff=max(est1.go,est2.go,na.rm=TRUE)
}
if(logic.go=='or'){
go_cutoff=min(est1.go,est2.go,na.rm=TRUE)
}
if(logic.nogo=='and')
{
nogo_cutoff=min(est1.nogo,est2.nogo,na.rm=TRUE)
}
if(logic.nogo=='or')
{
nogo_cutoff=max(est1.nogo,est2.nogo,na.rm=TRUE)
}
if(go_cutoff>=nogo_cutoff){return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
else{
overlap.flag=1
return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
if(all(flag==0)==FALSE){return(list(cutoff=c(NA,NA),flag=flag,overlap=overlap.flag))}
}
if(direction=='Less'){
if(false.go.CT1==TRUE){
est1.go<-findSAScutoff(n=n,CT=CT1.go,rate=1-FGR.CT1,direction='Less')
}
if(false.go.CT2==TRUE){
est2.go<-findSAScutoff(n=n,CT=CT2.go,rate=1-FGR.CT2,direction='Less')
}
if(false.nogo.CT1==TRUE){
est1.nogo<-findSAScutoff(n=n,CT=CT1.nogo,rate=1-FNGR.CT1,direction='Greater')
}
if(false.nogo.CT2==TRUE){
est2.nogo<-findSAScutoff(n=n,CT=CT2.nogo,rate=1-FNGR.CT2,direction='Greater')
}
if(any(is.na(c(est1.go,est2.go)))){logic.go='and'}
if(any(is.na(c(est1.nogo,est2.nogo)))){logic.nogo='and'}
if(logic.go=='and'){
go_cutoff=min(est1.go,est2.go,na.rm=TRUE)
}
if(logic.go=='or'){
go_cutoff=max(est1.go,est2.go,na.rm=TRUE)
}
if(logic.nogo=='and')
{
nogo_cutoff=max(est1.nogo,est2.nogo,na.rm=TRUE)
}
if(logic.nogo=='or')
{
nogo_cutoff=min(est1.nogo,est2.nogo,na.rm=TRUE)
}
if(go_cutoff<=nogo_cutoff){return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
else{
overlap.flag=1
return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
if(all(flag==0)==FALSE){return(list(cutoff=c(NA,NA),flag=flag,overlap=overlap.flag))}
}
}
if(para.exp==FALSE){
# The same as SAB
temp<-SAB_Bin_Cutoff(n=n,a=a,b=b,
CT1.go=CT1.go,
false.go.CT1=false.go.CT1,FGR.CT1=FGR.CT1,
CT1.nogo=CT1.nogo,
false.nogo.CT1=false.nogo.CT1,FNGR.CT1=FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2=false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2=false.nogo.CT2, FNGR.CT2=FNGR.CT2,
method=method,direction=direction,
logic.go=logic.go,logic.nogo=logic.nogo)
return(list(cutoff=temp$cutoff/n,flag=temp$flag,overlap=temp$overlap))
# if(method=='Bayesian'){
# temp<-SAB_Bin_Cutoff(n=n,a=a,b=b,
# CT1.go=CT1.go,
# false.go.CT1=false.go.CT1,FGR.CT1=FGR.CT1,
# CT1.nogo=CT1.nogo,
# false.nogo.CT1=false.nogo.CT1,FNGR.CT1=FNGR.CT1,
# CT2.go=CT2.go,
# false.go.CT2=false.go.CT2, FGR.CT2=FGR.CT2,
# CT2.nogo=CT2.nogo,
# false.nogo.CT2=false.nogo.CT2, FNGR.CT2=FNGR.CT2,
# method=method,direction=direction,
# logic.go=logic.go,logic.nogo=logic.nogo)
# return(list(cutoff=temp$cutoff/n,flag=temp$flag,overlap=temp$overlap))
# }
# if(method=='Frequentist'){
# if(direction=='Greater'){
#
# if(false.go.CT1==TRUE){
# sd.n1=sqrt(CT1.go*(1-CT1.go)/n)
# est1.go<-qnorm(1-FGR.CT1,mean=CT1.go,sd=sd.n1)
# }
# if(false.go.CT2==TRUE){
# sd.n2=sqrt(CT2.go*(1-CT2.go)/n)
# est2.go<-qnorm(1-FGR.CT2,mean=CT2.go,sd=sd.n2)
# }
# if(false.nogo.CT1==TRUE){
# sd.n1=sqrt(CT1.nogo*(1-CT1.nogo)/n)
# est1.nogo<-qnorm(FNGR.CT1,mean=CT1.nogo,sd=sd.n1)
# }
# if(false.nogo.CT2==TRUE){
# sd.n2=sqrt(CT2.nogo*(1-CT2.nogo)/n)
# est2.nogo<-qnorm(FNGR.CT2,mean=CT2.nogo,sd=sd.n2)
# }
#
#
# if(any(is.na(c(est1.go,est2.go)))){logic.go='and'}
# if(any(is.na(c(est1.nogo,est2.nogo)))){logic.nogo='and'}
# if(logic.go=='and'){
# go_cutoff=max(est1.go,est2.go,na.rm=TRUE)
# }
# if(logic.go=='or'){
# go_cutoff=min(est1.go,est2.go,na.rm=TRUE)
# }
# if(logic.nogo=='and')
# {
# nogo_cutoff=min(est1.nogo,est2.nogo,na.rm=TRUE)
# }
# if(logic.nogo=='or')
# {
# nogo_cutoff=max(est1.nogo,est2.nogo,na.rm=TRUE)
# }
#
#
# if(go_cutoff>=nogo_cutoff){return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
# else{
# overlap.flag=1
# return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
# if(all(flag==0)==FALSE){return(list(cutoff=c(NA,NA),flag=flag,overlap=overlap.flag))}
#
# }
# if(direction=='Less'){
# if(false.go.CT1==TRUE){
# sd.n1=sqrt(CT1.go*(1-CT1.go)/n)
# est1.go<-qnorm(FGR.CT1,mean=CT1.go,sd=sd.n1)
# }
# if(false.go.CT2==TRUE){
# sd.n2=sqrt(CT2.go*(1-CT2.go)/n)
# est2.go<-qnorm(FGR.CT2,mean=CT2.go,sd=sd.n2)
# }
# if(false.nogo.CT1==TRUE){
# sd.n1=sqrt(CT1.nogo*(1-CT1.nogo)/n)
# est1.nogo<-qnorm(1-FNGR.CT1,mean=CT1.nogo,sd=sd.n1)
# }
# if(false.nogo.CT2==TRUE){
# sd.n2=sqrt(CT2.nogo*(1-CT2.nogo)/n)
# est2.nogo<-qnorm(1-FNGR.CT2,mean=CT2.nogo,sd=sd.n2)
# }
#
# if(any(is.na(c(est1.go,est2.go)))){logic.go='and'}
# if(any(is.na(c(est1.nogo,est2.nogo)))){logic.nogo='and'}
# if(logic.go=='and'){
# go_cutoff=min(est1.go,est2.go,na.rm=TRUE)
# }
# if(logic.go=='or'){
# go_cutoff=max(est1.go,est2.go,na.rm=TRUE)
# }
# if(logic.nogo=='and')
# {
# nogo_cutoff=max(est1.nogo,est2.nogo,na.rm=TRUE)
# }
# if(logic.nogo=='or')
# {
# nogo_cutoff=min(est1.nogo,est2.nogo,na.rm=TRUE)
# }
#
# if(go_cutoff<=nogo_cutoff){return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
# else{
# overlap.flag=1
# return(list(cutoff=c(go_cutoff,nogo_cutoff),flag=flag,overlap=overlap.flag))}
# if(all(flag==0)==FALSE){return(list(cutoff=c(NA,NA),flag=flag,overlap=overlap.flag))}
# }
#
# }
}
}
####################################################################
Fix_SS_SAS_Survival_Prob<-function(npatients=126,
a=1,b=1,
mean=c(0.15,0.9),
CT1.go=0.25,
false.go.CT1=TRUE,FGR.CT1=0.25,
CT1.nogo=0.25,
false.nogo.CT1=TRUE,FNGR.CT1=0.25,
CT2.go=0.3,
false.go.CT2=TRUE, FGR.CT2=0.5,
CT2.nogo=0.3,
false.nogo.CT2=TRUE, FNGR.CT2=0.5,
overlap.option='GO',plot.figure=TRUE,
method='Bayesian',direction='Greater',
seed.num=369,para.exp=TRUE,
logic.go='and',logic.nogo='or'){
# n=ifelse(eventinput,nevents,npatients*maturity)
n=npatients
minmean=min(mean)
maxmean=max(mean)
meanseq=seq(minmean,maxmean,0.05)
go_prob<-rep(NA,length(mean))
nogo_prob<-rep(NA,length(mean))
inconclusive_prob<-rep(NA,length(mean))
go_prob_plot<-rep(NA,length(meanseq))
nogo_prob_plot<-rep(NA,length(meanseq))
inconclusive_prob_plot<-rep(NA,length(meanseq))
index=1
unsatisfied.flag=0
overlap.flag=0
if(para.exp==FALSE){
sd.n=sqrt(mean*(1-mean)/n)
sd.nseq=sqrt(meanseq*(1-meanseq)/n)
}
if(para.exp==TRUE){
lambdat=-log(mean)
lambdatseq=-log(meanseq)
var=(lambdat)^2*mean^2/n
varseq=(lambdatseq)^2*meanseq^2/n
sd.n=sqrt(var)
sd.nseq=sqrt(varseq)
}
temp=SAS_Survival_Cutoff(npatients=n,
a=a,b=b,
CT1.go=CT1.go,
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo = CT1.nogo,
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2 = false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method=method,direction=direction,
seed.num=seed.num,para.exp = para.exp,
logic.go = logic.go,logic.nogo = logic.nogo)
###SAS####
true_go_cutoff<-ifelse(temp$overlap==0,temp$cutoff[1],(overlap.option=='GO')*temp$cutoff[1]+(overlap.option=='NOGO')*(temp$cutoff[2]))
true_nogo_cutoff<-ifelse(temp$overlap==0,temp$cutoff[2],(overlap.option=='GO')*temp$cutoff[1]+(overlap.option=='NOGO')*(temp$cutoff[2]))
if (para.exp==FALSE){
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=1-pbinom(temp$cutoff[1]*n-1,n,mean)
nogo_prob=pbinom(temp$cutoff[2]*n-1,n,mean)
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=1-pbinom(temp$cutoff[1]*n-1,n,meanseq)
nogo_prob_plot=pbinom(temp$cutoff[2]*n-1,n,meanseq)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=1-pbinom(temp$cutoff[1]*n-1,n,mean)
nogo_prob=1-go_prob
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=1-pbinom(temp$cutoff[1]*n-1,n,meanseq)
nogo_prob_plot=1-go_prob_plot
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob=1-pbinom(temp$cutoff[2]*n-1,n,mean)
nogo_prob=1-go_prob
inconclusive_prob=1-go_prob-nogo_prob
nogo_prob_plot=pbinom(temp$cutoff[2]*n-1,n,meanseq)
go_prob_plot=1-nogo_prob_plot
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(mean))
go_prob=rep(NA,length(mean))
inconclusive_prob=rep(NA,length(mean))
}
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=pbinom(temp$cutoff[1]*n,n,mean)
nogo_prob=1-pbinom(temp$cutoff[2]*n,n,mean)
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=pbinom(temp$cutoff[1]*n,n,meanseq)
nogo_prob_plot=1-pbinom(temp$cutoff[2]*n,n,meanseq)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=pbinom(temp$cutoff[1]*n,n,mean)
nogo_prob=1-go_prob
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=pbinom(temp$cutoff[1]*n,n,meanseq)
nogo_prob_plot=1-go_prob_plot
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob=pbinom(temp$cutoff[2]*n,n,mean)
nogo_prob=1-go_prob
inconclusive_prob=1-go_prob-nogo_prob
go_prob_plot=pbinom(temp$cutoff[2]*n,n,meanseq)
nogo_prob_plot=1-go_prob_plot
inconclusive_prob_plot=1-go_prob-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(mean))
go_prob=rep(NA,length(mean))
inconclusive_prob=rep(NA,length(mean))
}
}
}
if (para.exp==TRUE){
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=1-pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
nogo_prob=pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
go_prob_plot=1-pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[1]>=1)){
go_prob[temp$cutoff[1]>=1]=0
go_prob_plot[temp$cutoff[1]>=1]=0
}
if(any(temp$cutoff[1]<=0)){
go_prob[temp$cutoff[1]<=0]=1
go_prob_plot[temp$cutoff[1]<=0]=1
}
if(any(temp$cutoff[2]<=0)){
nogo_prob[temp$cutoff[2]<=0]=0
nogo_prob_plot[temp$cutoff[2]<=0]=0
}
if(any(temp$cutoff[2]>=1)){
nogo_prob[temp$cutoff[2]>=1]=1
nogo_prob_plot[temp$cutoff[2]>=1]=1
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=1-pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
nogo_prob=pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
go_prob_plot=1-pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[1]>=1)){
go_prob[temp$cutoff[1]>=1]=0
go_prob_plot[temp$cutoff[1]>=1]=0
}
if(any(temp$cutoff[1]<=0)){
go_prob[temp$cutoff[1]<=0]=1
go_prob_plot[temp$cutoff[1]<=0]=1
}
if(any(temp$cutoff[1]<=0)){
nogo_prob[temp$cutoff[1]<=0]=0
nogo_prob_plot[temp$cutoff[1]<=0]=0
}
if(any(temp$cutoff[1]>=1)){
nogo_prob[temp$cutoff[1]>=1]=1
nogo_prob_plot[temp$cutoff[1]>=1]=1
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob=1-pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
nogo_prob=pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
go_prob_plot=1-pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[2]>=1)){
go_prob[temp$cutoff[2]>=1]=0
go_prob_plot[temp$cutoff[2]>=1]=0
}
if(any(temp$cutoff[2]<=0)){
go_prob[temp$cutoff[2]<=0]=1
go_prob_plot[temp$cutoff[2]<=0]=1
}
if(any(temp$cutoff[2]<=0)){
nogo_prob[temp$cutoff[2]<=0]=0
nogo_prob_plot[temp$cutoff[2]<=0]=0
}
if(any(temp$cutoff[2]>=1)){
nogo_prob[temp$cutoff[2]>=1]=1
nogo_prob_plot[temp$cutoff[2]>=1]=1
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(mean))
go_prob=rep(NA,length(mean))
inconclusive_prob=rep(NA,length(mean))
}
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
nogo_prob=1-pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
go_prob_plot=pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=1-pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[1]>=1)){
go_prob[temp$cutoff[1]>=1]=1
go_prob_plot[temp$cutoff[1]>=1]=1
}
if(any(temp$cutoff[1]<=0)){
go_prob[temp$cutoff[1]<=0]=0
go_prob_plot[temp$cutoff[1]<=0]=0
}
if(any(temp$cutoff[2]<=0)){
nogo_prob[temp$cutoff[2]<=0]=1
nogo_prob_plot[temp$cutoff[2]<=0]=1
}
if(any(temp$cutoff[2]>=1)){
nogo_prob[temp$cutoff[2]>=1]=0
nogo_prob_plot[temp$cutoff[2]>=1]=0
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
nogo_prob=1-pnorm(temp$cutoff[1],mean=mean,sd=sd.n)
go_prob_plot=pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=1-pnorm(temp$cutoff[1],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[1]>=1)){
go_prob[temp$cutoff[1]>=1]=1
go_prob_plot[temp$cutoff[1]>=1]=1
}
if(any(temp$cutoff[1]<=0)){
go_prob[temp$cutoff[1]<=0]=0
go_prob_plot[temp$cutoff[1]<=0]=0
}
if(any(temp$cutoff[1]<=0)){
nogo_prob[temp$cutoff[1]<=0]=1
nogo_prob_plot[temp$cutoff[1]<=0]=1
}
if(any(temp$cutoff[1]>=1)){
nogo_prob[temp$cutoff[1]>=1]=0
nogo_prob_plot[temp$cutoff[1]>=1]=0
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob=pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
nogo_prob=1-pnorm(temp$cutoff[2],mean=mean,sd=sd.n)
go_prob_plot=pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
nogo_prob_plot=1-pnorm(temp$cutoff[2],mean=meanseq,sd=sd.nseq)
if(any(temp$cutoff[2]>=1)){
go_prob[temp$cutoff[2]>=1]=1
go_prob_plot[temp$cutoff[2]>=1]=1
}
if(any(temp$cutoff[2]<=0)){
go_prob[temp$cutoff[2]<=0]=0
go_prob_plot[temp$cutoff[2]<=0]=0
}
if(any(temp$cutoff[2]<=0)){
nogo_prob[temp$cutoff[2]<=0]=1
nogo_prob_plot[temp$cutoff[2]<=0]=1
}
if(any(temp$cutoff[2]>=1)){
nogo_prob[temp$cutoff[2]>=1]=0
nogo_prob_plot[temp$cutoff[2]>=1]=0
}
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(mean))
go_prob=rep(NA,length(mean))
inconclusive_prob=rep(NA,length(mean))
}
}
}
########################################################################
if(plot.figure==TRUE){
delta=meanseq
p_nogo=nogo_prob_plot*100
p_grey=inconclusive_prob_plot*100
p_go=go_prob_plot*100
if(true_go_cutoff!=true_nogo_cutoff){
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
plot(delta,p_go,xlab=expression(paste("Survival probablity",sep="")),
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=range(delta),
ylim=c(0,100),type="n",axes=F)
axis(1, at=seq(minmean,maxmean,0.05), labels=T)
axis(2, at=seq(0,100,10),labels=T)
#text(rep(-0.6,6),seq(0,100,20),seq(0,100,20),adj=1,xpd=T)
box()
#abline(h=seq(0,100,10),lty=3,col=rgb(0.8,0.8,0.8),lwd=1)
points(delta,p_nogo,type="b",pch=16,col=rgb(0.9,0,0),lwd=3,lty=1)
points(delta,p_grey,type="b",pch=4,col=rgb(0.9,0.6,0),lwd=3,lty=6)
points(delta,p_go,type="b",pch=2,col=rgb(0,0.7,0),lwd=3,lty=2)
# if(eventinput==TRUE){
# text(min(mean),140,bquote(Number~of~events==~.(nevents)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
# }
# if(eventinput==FALSE){
# text(min(mean),140,bquote(Number~of~patients==~.(npatients)~';'~Maturity==~.(maturity)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
# }
text(min(mean),140,bquote(Number~of~patients==~.(npatients)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
if(overlap.flag==0&unsatisfied.flag==0){
if(direction=='Greater'){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
text(minmean,110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
if(direction=='Less'){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
text(minmean,110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=='GO'){
if(direction=="Greater"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(direction=="Less"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=='NOGO'){
if(direction=="Greater"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(direction=="Less"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
if(unsatisfied.flag==1){
text(minmean,130,paste0('Warning: We could not find classification of zones to satisfy your both GO and NOGO criterions at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(minmean,120,paste0('Please check and modify your desicion rule or the range of sample size!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
}
#######################################################################
if(plot.figure==TRUE){
delta=meanseq
p_nogo=nogo_prob_plot*100
p_grey=inconclusive_prob_plot*100
p_go=go_prob_plot*100
if(true_go_cutoff==true_nogo_cutoff){
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
plot(delta,p_go,xlab=expression(paste("Survival probablity",sep="")),
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=range(delta),
ylim=c(0,100),type="n",axes=F)
axis(1, at=seq(minmean,maxmean,0.05), labels=T)
axis(2, at=seq(0,100,10),labels=T)
#text(rep(-0.6,6),seq(0,100,20),seq(0,100,20),adj=1,xpd=T)
box()
#abline(h=seq(0,100,10),lty=3,col=rgb(0.8,0.8,0.8),lwd=1)
points(delta,p_nogo,type="b",pch=16,col=rgb(0.9,0,0),lwd=3,lty=1)
#points(delta,p_grey,type="b",pch=4,col=rgb(0.9,0.6,0),lwd=3,lty=6)
points(delta,p_go,type="b",pch=2,col=rgb(0,0.7,0),lwd=3,lty=2)
# if(eventinput==TRUE){
# text(min(mean),140,bquote(Number~of~events==~.(nevents)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
# }
# if(eventinput==FALSE){
# text(min(mean),140,bquote(Number~of~patients==~.(npatients)~';'~Maturity==~.(maturity)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
# }
text(min(mean),140,bquote(Number~of~patients==~.(npatients)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
if(overlap.flag==0&unsatisfied.flag==0){
if(direction=='Greater'){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
if(direction=='Less'){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=='GO'){
if(direction=="Greater"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(direction=="Less"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=='NOGO'){
if(direction=="Greater"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(direction=="Less"){
text(minmean,130,bquote(GO~symbol("\336")~Observed~survival~probability~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(minmean,120,bquote(NOGO~symbol("\336")~Observed~survival~probability~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(minmean,115,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(minmean,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
if(unsatisfied.flag==1){
text(minmean,130,paste0('Warning: We could not find classification of zones to satisfy your both GO and NOGO criterions at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(minmean,120,paste0('Please check and modify your desicion rule or the range of sample size!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
}
#######################################################################
return(list(go_prob=go_prob,nogo_prob=nogo_prob,
inconclusive_prob=inconclusive_prob,
overlap.flag=overlap.flag,overlap.option=overlap.option,
unsatisfied.flag=unsatisfied.flag,cutoff=temp$cutoff,true_cutoff=c(true_go_cutoff,true_nogo_cutoff))
)
}
Vary_SS_SAS_Survival_Prob<-function(npatientsmin=100,npatientsmax=500,
a=1/3,b=1,
mean=0.3,
CT1.go=0.25,
false.go.CT1=TRUE,FGR.CT1=0.25,
CT1.nogo=0.3,
false.nogo.CT1=TRUE,FNGR.CT1=0.25,
CT2.go=0.3,
false.go.CT2=TRUE, FGR.CT2=0.5,
CT2.nogo=0.3,
false.nogo.CT2=TRUE, FNGR.CT2=0.5,
overlap.option='GO',plot.cutoff=TRUE,plot.prob=TRUE,
method="Bayesian",direction="Greater",
seed.num=369,para.exp=TRUE,
logic.go='and',logic.nogo='or'){
# nmin=ifelse(eventinput,neventsmin,npatientsmin*maturity)
# nmax=ifelse(eventinput,neventsmax,npatientsmax*maturity)
nmin=npatientsmin
nmax=npatientsmax
nseq=unique(round(c(seq(nmin,(nmax+nmin)/2,length=6)[-6],seq((nmax+nmin)/2,nmax,length=6))))
go_prob<-matrix(NA,ncol=length(nseq),nrow=length(mean))
nogo_prob<-matrix(NA,ncol=length(nseq),nrow=length(mean))
inconclusive_prob<-matrix(NA,ncol=length(nseq),nrow=length(mean))
go_cutoff<-rep(NA,length(nseq))
nogo_cutoff<-rep(NA,length(nseq))
index=1
n_unsatisfied=NA
n_overlap=NA
if (para.exp==FALSE){
for(i in nseq){
temp=SAS_Survival_Cutoff(npatients=i,
a=a,b=b,
CT1.go=CT1.go,
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo = CT1.nogo,
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2 = false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method=method,direction=direction,
seed.num=seed.num,para.exp = para.exp,
logic.go=logic.go,logic.nogo=logic.nogo)
overlap = temp$overlap
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob[,index]=1-pbinom(temp$cutoff[1]*i-1,i,mean)
nogo_prob[,index]=pbinom(temp$cutoff[2]*i-1,i,mean)
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob[,index]=1-pbinom(temp$cutoff[1]*i-1,i,mean)
nogo_prob[,index]=pbinom(temp$cutoff[1]*i-1,i,mean)
inconclusive_prob[,index]=0
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob[,index]=1-pbinom(temp$cutoff[2]*i-1,i,mean)
nogo_prob[,index]=pbinom(temp$cutoff[2]*i-1,i,mean)
inconclusive_prob[,index]=0
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)==FALSE){
n_unsatisfied=c(n_unsatisfied,i)
}
index=index+1
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob[,index]=pbinom(temp$cutoff[1]*i,i,mean)
nogo_prob[,index]=1-pbinom(temp$cutoff[2]*i,i,mean)
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob[,index]=pbinom(temp$cutoff[1]*i,i,mean)
nogo_prob[,index]=1-pbinom(temp$cutoff[1]*i,i,mean)
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob[,index]=pbinom(temp$cutoff[2]*i,i,mean)
nogo_prob[,index]=1-pbinom(temp$cutoff[2]*i,i,mean)
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)==FALSE){
n_unsatisfied=c(n_unsatisfied,i)
}
index=index+1
}
}
}
if (para.exp==TRUE){
ncore <- detectCores()
cl<-makeCluster(ncore)
registerDoParallel(cl)
results<-foreach(i = nseq,.export=c('SAS_Survival_Cutoff',"SAB_Bin_Cutoff"),.combine=rbind) %dopar% {
temp=SAS_Survival_Cutoff(npatients=i,
a=a,b=b,
CT1.go=CT1.go,
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo = CT1.nogo,
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2 = false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method=method,direction=direction,
seed.num=seed.num,para.exp = para.exp,
logic.go=logic.go,logic.nogo=logic.nogo)
c(temp$overlap,temp$cutoff)
}
stopCluster(cl)
go_cutoff<-results[,2]
nogo_cutoff<-results[,3]
overlap<-results[,1]
for(mean.index in 1:length(mean)){
mean_temp=mean[mean.index]
sd.i=sqrt(mean_temp*(1-mean_temp)/nseq)
if(para.exp==FALSE){
sd.i=sqrt(mean_temp*(1-mean_temp)/nseq)
}
if(para.exp==TRUE){
lambdat=-log(mean_temp)
var=(lambdat)^2*mean_temp^2/nseq
sd.i=sqrt(var)
}
true_go_cutoff<-go_cutoff
true_nogo_cutoff<-nogo_cutoff
if(direction=='Greater'){
true_go_cutoff[overlap==1]<-(overlap.option=='GO')*go_cutoff[overlap==1]+(overlap.option=='NOGO')*nogo_cutoff[overlap==1]
true_nogo_cutoff[overlap==1]<-(overlap.option=='GO')*go_cutoff[overlap==1]+(overlap.option=='NOGO')*nogo_cutoff[overlap==1]
go_prob[mean.index,]=1-pnorm(true_go_cutoff,mean=mean_temp,sd=sd.i)
nogo_prob[mean.index,]=pnorm(true_nogo_cutoff,mean=mean_temp,sd=sd.i)
if(any(true_go_cutoff>=1)){
go_prob[mean.index,true_go_cutoff>=1]=0
}
if(any(true_go_cutoff<=0)){
go_prob[mean.index,cutoff<=0]=1
}
if(any(true_nogo_cutoff<=0)){
nogo_prob[mean.index,true_nogo_cutoff<=0]=0
}
if(any(true_nogo_cutoff>=1)){
nogo_prob[mean.index,true_nogo_cutoff>=1]=1
}
inconclusive_prob[mean.index,]=1-go_prob[mean.index,]-nogo_prob[mean.index,]
}
if(direction=='Less'){
true_go_cutoff[overlap==1]<-(overlap.option=='GO')*go_cutoff[overlap==1]+(overlap.option=='NOGO')*nogo_cutoff[overlap==1]
true_nogo_cutoff[overlap==1]<-(overlap.option=='GO')*go_cutoff[overlap==1]+(overlap.option=='NOGO')*nogo_cutoff[overlap==1]
go_prob[mean.index,]=pnorm(true_go_cutoff,mean=mean_temp,sd=sd.i)
nogo_prob[mean.index,]=1-pnorm(true_nogo_cutoff,mean=mean_temp,sd=sd.i)
if(any(true_go_cutoff>=1)){
go_prob[mean.index,true_go_cutoff>=1]=1
}
if(any(true_go_cutoff<=0)){
go_prob[mean.index,cutoff<=0]=0
}
if(any(true_nogo_cutoff<=0)){
nogo_prob[mean.index,true_nogo_cutoff<=0]=1
}
if(any(true_nogo_cutoff>=1)){
nogo_prob[mean.index,true_nogo_cutoff>=1]=0
}
inconclusive_prob[mean.index,]=1-go_prob[mean.index,]-nogo_prob[mean.index,]
}
}
}
n_overlap=n_overlap[-1]
n_unsatisfied=n_unsatisfied[-1]
####plot figure
if(plot.prob==TRUE){
for(j in 1:length(mean)){
p_go=go_prob[j,]*100
p_nogo=nogo_prob[j,]*100
class(p_nogo)
dim(p_nogo)
p_grey=100-p_go-p_nogo
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
cum_p_nogo=p_nogo
cum_p_grey=p_nogo+p_grey
cum_p_go=p_nogo+p_grey+p_go
delta=nseq
plot(delta,cum_p_go,xlab="Sample size",
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=c(nmin,nmax),
ylim=c(0,100),type="n",axes=F,pch=NA)
axis(1, at=nseq, labels=T)
axis(2, at=seq(0,100,10),labels=T)
#text(rep(-0.6,6),seq(0,100,20),seq(0,100,20),adj=1,xpd=T)
box()
#abline(h=seq(0,100,10),lty=3,col=rgb(0.8,0.8,0.8),lwd=1)
polygon(c(delta,rev(delta)),c(cum_p_nogo,rev(rep(0,length(delta)))),col=rgb(0.9,0,0),border=NA)
polygon(c(delta,rev(delta)),c(cum_p_grey,rev(cum_p_nogo)),col=rgb(0.9,0.6,0),border=NA)
polygon(c(delta,rev(delta)),c(cum_p_go,rev(cum_p_grey)),col=rgb(0,0.7,0),border=NA)
text((nmin+nmax)/2,120,paste0("True survival probability=",round(mean[j],3)),xpd=T,adj=0.5,cex=0.8)
if(any(overlap!=0)){
text(nmin,115,paste0('Warning: GO and NOGO cut-offs are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(nmin,110,paste(n_overlap),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#print(n_overlap)
text(nmin,110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
}
if(plot.cutoff==TRUE){
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
ylim_max=max(c(go_cutoff,nogo_cutoff,round(CT1.go,3),round(CT2.go,3),round(CT1.nogo,3),round(CT2.nogo,3)),na.rm=TRUE)+0.1
ylim_min=min(c(go_cutoff,nogo_cutoff,round(CT1.go,3),round(CT2.go,3),round(CT1.nogo,3),round(CT2.nogo,3)),na.rm=TRUE)-0.1
plot(NA,NA,xlab='Sample size',ylab="Observed survival probability",xlim=c(max(0,range(nseq)[1]-10),range(nseq)[2]+10),ylim=c(ylim_min,ylim_max),type="n",axes=F,col=rgb(1,0,0),lty=1,lwd=2)
axis(1, at=nseq, labels=T)
axis(2, at=round(c(seq(ylim_min,ylim_max,round((ylim_max-ylim_min)/10,digits=2)),CT1.go,CT2.go,CT1.nogo,CT2.nogo),digits=2),labels=T)
#text(rep(-0.6,6),seq(0,100,20),seq(0,300,20),adj=1,xpd=T)
box()
lines(nseq,nogo_cutoff,col=rgb(0.9,0,0),lwd=2)
lines(nseq,go_cutoff,col=rgb(0,0.7,0),lwd=2)
legend('bottomright',legend=c("Cut off of GO","Cut off of NOGO"),
col=c(rgb(0,0.7,0),rgb(0.9,0,0)),
lwd=c(2,2),
lty=c(1,1),cex=0.5)
if(any(overlap!=0)){
text(nmin,ylim_max+abs(ylim_max-ylim_min)/10,paste0('Warning: GO and NOGO zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(nmin,110,paste(n_overlap),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#print(n_overlap)
text(nmin,ylim_max+abs(ylim_max-ylim_min)/20,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
}
return(list(overlap))
}
Interim_SAS<-function(num_interim=3,interim_n=c(50,100,150),
CT1.go=c(0.25,0.25,0.25),
false.go.CT1=c(TRUE,TRUE,TRUE),FGR.CT1=c(0.25,0.25,0.25),
CT1.nogo=c(0.25,0.25,0.25),
false.nogo.CT1=c(TRUE,TRUE,TRUE),FNGR.CT1=c(0.25,0.25,0.25),
CT2.go=c(0.25,0.25,0.25),
false.go.CT2=c(TRUE,TRUE,TRUE),FGR.CT2=c(0.25,0.25,0.25),
CT2.nogo=c(0.25,0.25,0.25),
false.nogo.CT2=c(TRUE,TRUE,TRUE),FNGR.CT2=c(0.25,0.25,0.25),
overlap.option=c('GO','GO','GO'),
method='Bayesian',direction=c("Greater",'Greater','Greater'),nsim_IA=10000,seed.num=369,
task=c('Futility','Superiority','Futility and superiority'),
logic.go=c('and','or','and'),
logic.nogo=c('and','or','or'),
a=1/3,b=1,mean=c(0.25),para.exp=TRUE){
# if(eventinput==FALSE){
# interim_n=floor(interim_n*maturity)
# }
interim_n=sort(interim_n)
go_cutoff<-rep(NA,num_interim)
nogo_cutoff<-rep(NA,num_interim)
true_go_cutoff<-rep(NA,num_interim)
true_nogo_cutoff<-rep(NA,num_interim)
overlap<-rep(NA,num_interim)
for(i in 1:num_interim){
temp<-SAS_Survival_Cutoff(npatients=interim_n[i],a=a,b=b,
CT1.go=CT1.go[i],
false.go.CT1=false.go.CT1[i],FGR.CT1=FGR.CT1[i],
CT1.nogo=CT1.nogo[i],
false.nogo.CT1=false.nogo.CT1[i],FNGR.CT1=FNGR.CT1[i],
CT2.go=CT2.go[i],
false.go.CT2=false.go.CT2[i], FGR.CT2=FGR.CT2[i],
CT2.nogo=CT2.nogo[i],
false.nogo.CT2=false.nogo.CT2[i], FNGR.CT2=FNGR.CT2[i],
method=method,direction=direction[i],
seed.num=seed.num,para.exp=para.exp,
logic.go=logic.go[i],logic.nogo=logic.nogo[i])
go_cutoff[i]<-temp$cutoff[1]
nogo_cutoff[i]<-temp$cutoff[2]
overlap[i]<-temp$overlap
###SAS####
true_go_cutoff[i]<-ifelse(overlap[i]==0,go_cutoff[i],(overlap.option[i]=='GO')*go_cutoff[i]+(overlap.option[i]=='NOGO')*(nogo_cutoff[i]))
true_nogo_cutoff[i]<-ifelse(overlap[i]==0,nogo_cutoff[i],(overlap.option[i]=='GO')*(go_cutoff[i])+(overlap.option[i]=='NOGO')*(nogo_cutoff[i]))
####
}
#####
temptable=c()
for(meanindex in 1:length(mean)){
set.seed(seed.num)
###SAS####
sim_data<-matrix(NA,nrow=nsim_IA,ncol=max(interim_n))
# if(para.exp==FALSE){
# sd=sqrt(mean[meanindex]*(1-mean[meanindex]))
# }
if(para.exp==FALSE){
for(k in 1:max(interim_n)){
sim_data[,k]<- rbinom(nsim_IA,1,mean[meanindex])
}
}
if(para.exp==TRUE){
lambdat=-log(mean[meanindex])
var=(lambdat)^2*mean[meanindex]^2
sd=sqrt(var)
for(k in 1:max(interim_n)){
sim_data[,k]<- rnorm(nsim_IA,mean=mean[meanindex],sd=sd)
}
}
cum_sim_data_temp<-apply(sim_data,1,cumsum)
cum_sim_data<-t(cum_sim_data_temp[interim_n,]/interim_n)
#####
go_matrix<-matrix(NA,nrow=nsim_IA,ncol=num_interim)
nogo_matrix<-matrix(NA,nrow=nsim_IA,ncol=num_interim)
inconclusive_matrix<-matrix(NA,nrow=nsim_IA,ncol=num_interim)
table<-matrix(NA,ncol=num_interim+1,nrow=10)
IA_go_matrix<-matrix(NA,nrow=nsim_IA,ncol=num_interim) ###whether continue to next stage
for(j in 1:num_interim ){
if(direction[j]=='Greater'){
go_matrix[,j]<-cum_sim_data[,j]>=true_go_cutoff[j]
nogo_matrix[,j]<-cum_sim_data[,j]<true_nogo_cutoff[j]
inconclusive_matrix[,j]<-rep(1,nsim_IA)-go_matrix[,j]-nogo_matrix[,j]
}
}
for(j in 1:num_interim ){
if(direction[j]=='Less'){
go_matrix[,j]<-cum_sim_data[,j]<=true_go_cutoff[j]
nogo_matrix[,j]<-cum_sim_data[,j]>true_nogo_cutoff[j]
inconclusive_matrix[,j]<-rep(1,nsim_IA)-go_matrix[,j]-nogo_matrix[,j]
}
}
for(ii in 1:(num_interim)){
if(task[ii]=='Futility'){
IA_go_matrix[,ii]=inconclusive_matrix[,ii]+go_matrix[,ii]
}
if(task[ii]=='Superiority'){
IA_go_matrix[,ii]=inconclusive_matrix[,ii]+nogo_matrix[,ii]
}
if(task[ii]=='Futility and superiority'){
IA_go_matrix[,ii]=inconclusive_matrix[,ii]
}
}
cum_IA_go_matrix<-t(apply(IA_go_matrix,1,cumprod))
for(j in 1:(num_interim)){
table[1,j]=interim_n[j]
table[2,j]=mean[meanindex]
table[3,j]=task[j]
if(j==1){
if(task[j]=='Superiority'|task[j]=='Futility and superiority'){
table[4,j]=round(sum(go_matrix[,j]==1)/nsim_IA,3)}else{table[4,j]=0}
if(task[j]=='Futility'|task[j]=='Futility and superiority'){
table[6,j]=round(sum(nogo_matrix[,j]==1)/nsim_IA,3)
}else{table[6,j]=0}
}else{
if(task[j]=='Superiority'|task[j]=='Futility and superiority'){
table[4,j]=round(sum(go_matrix[,j]==1&cum_IA_go_matrix[,j-1]==1)/nsim_IA,3)}else{table[4,j]=0}
if(task[j]=='Futility'|task[j]=='Futility and superiority'){
table[6,j]=round(sum(nogo_matrix[,j]==1&cum_IA_go_matrix[,j-1]==1)/nsim_IA,3)
}else{table[6,j]=0}
}
table[5,j]=round(sum(cum_IA_go_matrix[,j]==1)/nsim_IA,3)
if(task[j]=='Futility'){
table[7,j]=ifelse(direction[j]=='Greater',HTML(paste0('-/<',round(true_nogo_cutoff[j],3))),HTML(paste0('-/>',round(true_nogo_cutoff[j],3))))
}
if(task[j]=='Superiority'){
table[7,j]=ifelse(direction[j]=='Greater',HTML(paste0('>=',round(true_go_cutoff[j],3),'/-')),HTML(paste0('<=',round(true_go_cutoff[j],3),'/-')))
}
if(task[j]=='Futility and superiority'){
table[7,j]=ifelse(direction[j]=='Greater',HTML(paste0('>=',round(true_go_cutoff[j],3),' /','<',round(true_nogo_cutoff[j],3))),HTML(paste0('<=',round(true_go_cutoff[j],3),' / ','>',round(true_nogo_cutoff[j],3))))
}
table[10,j]=ifelse(direction[j]=='Greater',HTML(paste0('>=',round(go_cutoff[j],3))),HTML(paste0('<=',round(go_cutoff[j],3))))
table[9,j]=ifelse(direction[j]=='Greater',HTML(paste0('<',round(nogo_cutoff[j],3))),HTML(paste0('>',round(nogo_cutoff[j],3))))
table[8,j]<-ifelse(overlap[j]==1,paste0('GO/NOGO zones overlapped, classified by criterion of ',overlap.option[j]),'None')
}
expectss<-round(sum(as.numeric(table[1,1:num_interim])*(c(as.numeric(table[4,1:num_interim-1])+as.numeric(table[6,1:num_interim-1]),as.numeric(table[5,num_interim-1])))),3)
table[1,num_interim+1]=HTML(paste0(expectss,' (expected)'))
table[2,num_interim+1]=mean[meanindex]
table[3,num_interim+1]=''
table[4,num_interim+1]=round(sum(as.numeric(table[4,1:num_interim])),3)
table[5,num_interim+1]=round(as.numeric(table[5,num_interim]),3)
table[6,num_interim+1]=round(sum(as.numeric(table[6,1:num_interim])),3)
table[7,num_interim+1]=''
table[8,num_interim+1]=''
table[9,num_interim+1]=''
table[10,num_interim+1]=''
table<-as.table(table)
tablecolname<-c(paste0('Interim analysis ',1:(num_interim-1)),'Final analysis',"Summary")
tablerowname<-c('Sample size','True survival probability','Task','Success','To next interim/final or inconclusive',
'Stop','Superority/Futility zone','Warning',
'Cut off for NOGO rule',
'Cut off for GO rule')
table<-cbind(tablerowname,rep(meanindex,10),table)
colnames(table)<-c(" ",'Setting',tablecolname)
temptable=rbind(temptable,table)
}
return(temptable)
}
# #Interim_SAS(interim_n = c(66,131,197),CT1.go=c(0.5,0.9,0.7),FGR.CT1=c(1-0.71,0.653,0.0238),FNGR.CT1=c(0.707,1-0.653,1-0.0238),CT2.go=c(NA,NA,NA,NA),method='Frequentist',direction=c('Greater','Less','Greater'),task=c('Futility','Superiority','Superiority'),mean=c(0.9,1.8))
#
# Fix_SS_SAS_Survival_Prob(eventinput=TRUE,nevents=100,
# npatients=180,maturity=0.7,
# a=1,b=1,
# mean=c(0.15,0.9),
# CT1.go=0.38,
# false.go.CT1=TRUE,FGR.CT1=0.7,
# CT1.nogo=0.38,
# false.nogo.CT1=TRUE,FNGR.CT1=0.2,
# CT2.go=0.3,
# false.go.CT2=TRUE, FGR.CT2=0.5,
# CT2.nogo=0.3,
# false.nogo.CT2=TRUE, FNGR.CT2=0.5,
# overlap.option='GO',plot.figure=TRUE,
# method='Bayesian',direction='Greater',
# seed.num=369,para.exp=FALSE,
# logic.go='and',logic.nogo='or')
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