R/TAS FUNCTION TWO DIRECTION.R
In AshZhong/Goahead:
Defines functions
Fix_SS_TAS_Survival_Prob
TAS_Survival_Cutoff
library(shiny)
TAS_Survival_Cutoff<-function(eventinput=TRUE,nevents=126,
npatients=180,maturity=0.7,
alloc.ratio=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='Bayesian',direction='Greater',
prior.mean=1,prior.sd=1,
logic.go='and',
logic.nogo='or'){
if(eventinput==TRUE){n=nevents}else{n=npatients*maturity}
n1=n*alloc.ratio/(1+alloc.ratio)
n2=n-n1
sd=sqrt(1/n1+1/n2)
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(method=='Frequentist'){
if(direction=='Less'){
if(false.go.CT1==TRUE){
est1.go=exp(qnorm(FGR.CT1,mean=log(CT1.go),sd=sd))
}
if(false.go.CT2==TRUE){
est2.go=exp(qnorm(FGR.CT2,mean=log(CT2.go),sd=sd))
}
if(false.nogo.CT1==TRUE){
est1.nogo=exp(qnorm(1-FNGR.CT1,mean=log(CT1.nogo),sd=sd))
}
if(false.nogo.CT2==TRUE){
est2.nogo=exp(qnorm(1-FNGR.CT2,mean=log(CT2.nogo),sd=sd))
}
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(direction=='Greater'){
if(false.go.CT1==TRUE){
est1.go=exp(qnorm(1-FGR.CT1,mean=log(CT1.go),sd=sd))
}
if(false.go.CT2==TRUE){
est2.go=exp(qnorm(1-FGR.CT2,mean=log(CT2.go),sd=sd))
}
if(false.nogo.CT1==TRUE){
est1.nogo=exp(qnorm(FNGR.CT1,mean=log(CT1.nogo),sd=sd))
}
if(false.nogo.CT2==TRUE){
est2.nogo=exp(qnorm(FNGR.CT2,mean=log(CT2.nogo),sd=sd))
}
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(method=='Bayesian'){
temp<-SAN_Normal_Cutoff(n=n,prior.mean = prior.mean,prior.sd=prior.sd,sd=sd*sqrt(n),
CT1.go=log(CT1.go),
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo=log(CT1.nogo),
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=log(CT2.go),
false.go.CT2 = false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=log(CT2.nogo),
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method='Bayesian',direction=direction,
fix.var=TRUE,noninfo=TRUE,
logic.go=logic.go,logic.nogo=logic.nogo)
return(list(cutoff=exp(temp$cutoff),flag=temp$flag,overlap=temp$overlap))
}
}
#####################################################
Fix_SS_TAS_Survival_Prob<-function(eventinput=TRUE,nevents=126,
npatients=180,maturity=0.7,
alloc.ratio=1,
CT1.go=10/14.6,
false.go.CT1=TRUE,FGR.CT1=0.1,
CT1.nogo=10/14.6,
false.nogo.CT1=TRUE,FNGR.CT1=0.1,
CT2.go=10/12,
false.go.CT2=TRUE, FGR.CT2=0.2,
CT2.nogo=10/12,
false.nogo.CT2=FALSE, FNGR.CT2=0.2,
RR=c(0.933,0.483),overlap.option='GO',plot.figure=TRUE,
method='Bayesian',direction='Less',prior.mean=1,prior.sd=1,
logic.go='and',logic.nogo='or'){
if(eventinput==TRUE){n=nevents}else{n=npatients*maturity}
n1=n*alloc.ratio/(1+alloc.ratio)
n2=n-n1
sd=sqrt(1/n1+1/n2)
go_prob<-rep(NA,length(RR))
nogo_prob<-rep(NA,length(RR))
inconclusive_prob<-rep(NA,length(RR))
index=1
unsatisfied.flag=0
overlap.flag=0
temp=TAS_Survival_Cutoff(eventinput=eventinput,nevents=nevents,
npatients=npatients,maturity=maturity,
alloc.ratio = alloc.ratio,
CT1.go=CT1.go,
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo = CT1.nogo,
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2 = false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method=method,direction=direction,
prior.mean=prior.mean,prior.sd=prior.sd,
logic.go = logic.go,logic.nogo=logic.nogo)
###TAS####
true_go_cutoff<-ifelse(temp$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$cutoff[2]))
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
nogo_prob=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
nogo_prob=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
nogo_prob=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
go_prob=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(RR))
go_prob=rep(NA,length(RR))
inconclusive_prob=rep(NA,length(RR))
}
}
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
nogo_prob=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
nogo_prob=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
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(log(temp$cutoff[2]),mean=log(RR),sd=sd)
nogo_prob=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(RR))
go_prob=rep(NA,length(RR))
inconclusive_prob=rep(NA,length(RR))
}
}
##########################################################################################
if(plot.figure==TRUE){
GOdirectsymbol=NA
NOGOdirectsymbol=NA
RRseq=seq(min(RR),max(RR),0.05)
go_prob_plot<-rep(NA,length(RRseq))
nogo_prob_plot<-rep(NA,length(RRseq))
inconclusive_prob_plot<-rep(NA,length(RRseq))
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
}
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
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_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
}
}
delta=RRseq
p_nogo=nogo_prob_plot*100
p_grey=inconclusive_prob_plot*100
p_go=go_prob_plot*100
if(true_go_cutoff!=true_nogo_cutoff){
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
plot(delta,p_go,xlab=expression(paste("True ",HR,sep="")),
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=range(delta),
ylim=c(0,100),type="n",axes=F)
axis(1, at=seq(min(RR),max(RR),0.05), labels=T)
axis(2, at=seq(0,100,10),labels=T)
box()
points(delta,p_nogo,type="b",pch=16,col=rgb(0.9,0,0),lwd=3,lty=1)
points(delta,p_grey,type="b",pch=4,col=rgb(0.9,0.6,0),lwd=3,lty=6)
points(delta,p_go,type="b",pch=2,col=rgb(0,0.7,0),lwd=3,lty=2)
if(eventinput==TRUE){
text(min(RR),140,bquote(Number~of~events==~.(nevents)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
}
if(eventinput==FALSE){
text(min(RR),140,bquote(Number~of~patients==~.(npatients)~';'~Maturity==~.(maturity)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
}
if(direction=='Greater'){
if(overlap.flag==0&unsatisfied.flag==0){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
text(min(RR),110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=="GO"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),115,paste0('Warning: GO and NOGO Zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(min(RR),110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=="NOGO"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),115,paste0('Warning: GO and NOGO Zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(min(RR),110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(unsatisfied.flag==1){
text(min(RR),130,paste0('Warning: We could not find classification of zones to satisfy your both GO and NOGO criterions at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(min(RR),120,paste0('Please check and modify your desicion rule or the range of sample size!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
if(direction=='Less'){
if(overlap.flag==0&unsatisfied.flag==0){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
text(min(RR),110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=="GO"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),115,paste0('Warning: GO and NOGO Zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(min(RR),110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=="NOGO"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),115,paste0('Warning: GO and NOGO Zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(min(RR),110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(unsatisfied.flag==1){
text(min(RR),130,paste0('Warning: We could not find classification of zones to satisfy your both GO and NOGO criterions at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(min(RR),120,paste0('Please check and modify your desicion rule or the range of sample size!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
}
}
###########################&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
if(plot.figure==TRUE){
GOdirectsymbol=NA
NOGOdirectsymbol=NA
RRseq=seq(min(RR),max(RR),0.05)
go_prob_plot<-rep(NA,length(RRseq))
nogo_prob_plot<-rep(NA,length(RRseq))
inconclusive_prob_plot<-rep(NA,length(RRseq))
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
}
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
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_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
}
}
delta=RRseq
p_nogo=nogo_prob_plot*100
p_grey=inconclusive_prob_plot*100
p_go=go_prob_plot*100
if(true_go_cutoff==true_nogo_cutoff){
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
plot(delta,p_go,xlab=expression(paste("True ",HR,sep="")),
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=range(delta),
ylim=c(0,100),type="n",axes=F)
axis(1, at=seq(min(RR),max(RR),0.05), labels=T)
axis(2, at=seq(0,100,10),labels=T)
box()
points(delta,p_nogo,type="b",pch=16,col=rgb(0.9,0,0),lwd=3,lty=1)
#points(delta,p_grey,type="b",pch=4,col=rgb(0.9,0.6,0),lwd=3,lty=6)
points(delta,p_go,type="b",pch=2,col=rgb(0,0.7,0),lwd=3,lty=2)
if(eventinput==TRUE){
text(min(RR),140,bquote(Number~of~events==~.(nevents)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
}
if(eventinput==FALSE){
text(min(RR),140,bquote(Number~of~patients==~.(npatients)~';'~Maturity==~.(maturity)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
}
if(direction=='Greater'){
if(overlap.flag==0&unsatisfied.flag==0){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=="GO"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),115,paste0('Warning: GO and NOGO Zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(min(RR),110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=="NOGO"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),115,paste0('Warning: GO and NOGO Zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(min(RR),110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(unsatisfied.flag==1){
text(min(RR),130,paste0('Warning: We could not find classification of zones to satisfy your both GO and NOGO criterions at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(min(RR),120,paste0('Please check and modify your desicion rule or the range of sample size!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
if(direction=='Less'){
if(overlap.flag==0&unsatisfied.flag==0){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),110,bquote(Inconclusive~symbol("\336")~anything~between~GO~and~NOGO),xpd=T,adj=0,cex=0.8,col=rgb(0.9,0.6,0))
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=="GO"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),115,paste0('Warning: GO and NOGO Zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(min(RR),110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(overlap.flag==1&unsatisfied.flag==0&overlap.option=="NOGO"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),115,paste0('Warning: GO and NOGO Zones are overlaped at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
#text(min(RR),110,paste0('The zones are classfied by cutoff of ', overlap.option),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0.6,0))
}
if(unsatisfied.flag==1){
text(min(RR),130,paste0('Warning: We could not find classification of zones to satisfy your both GO and NOGO criterions at some sample sizes'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(min(RR),120,paste0('Please check and modify your desicion rule or the range of sample size!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
}
}
######################################################################################
return(list(go_prob=go_prob,nogo_prob=nogo_prob,
inconclusive_prob=inconclusive_prob,
overlap.flag=overlap.flag,overlap.option=overlap.option,
unsatisfied.flag=unsatisfied.flag,cutoff=temp$cutoff,true_cutoff=c(true_go_cutoff,true_nogo_cutoff))
)
}
Vary_SS_TAS_Survival_Prob<-function(eventinput=TRUE,
neventsmin=70,neventsmax=350,
npatientsmin=100,npatientsmax=500,
maturity=0.7,
alloc.ratio=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,
RR=c(0.25),overlap.option='GO',
plot.cutoff=TRUE,plot.prob=TRUE,
method='Bayesian',direction='Greater',
prior.mean=1,prior.sd=1,
logic.go='and',logic.nogo='or'){
if(eventinput==TRUE){nmin=neventsmin
nmax=neventsmax
nseq=unique(round(c(seq(nmin,(nmax+nmin)/2,length=6)[-6],seq((nmax+nmin)/2,nmax,length=6))))
n1=nseq*alloc.ratio/(1+alloc.ratio)
n2=nseq*1/(1+alloc.ratio)
sdseq=sqrt(1/n1+1/n2)
xlabtitle=paste0('Number of events')}else{
nmin=npatientsmin
nmax=npatientsmax
nseq=seq(nmin,nmax,by=1)
n1=nseq*maturity*alloc.ratio/(1+alloc.ratio)
n2=nseq*maturity*1/(1+alloc.ratio)
sdseq=sqrt(1/n1+1/n2)
xlabtitle=paste0('Number of patients (Maturity= ',maturity,')')
}
go_prob<-matrix(NA,ncol=length(nseq),nrow=length(RR))
nogo_prob<-matrix(NA,ncol=length(nseq),nrow=length(RR))
inconclusive_prob<-matrix(NA,ncol=length(nseq),nrow=length(RR))
go_cutoff<-rep(NA,length(nseq))
nogo_cutoff<-rep(NA,length(nseq))
index=1
n_unsatisfied=NA
n_overlap=NA
for(i in nseq){
temp=TAS_Survival_Cutoff(eventinput = eventinput,
npatients = i,maturity = maturity,
nevents=i,alloc.ratio = alloc.ratio,
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,
prior.mean=prior.mean,prior.sd=prior.sd,
logic.go=logic.go,logic.nogo=logic.nogo)
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob[,index]=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
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]=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
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]=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
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$flag==0)==FALSE){
n_unsatisfied=c(n_unsatisfied,i)
}
}
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob[,index]=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob[,index]=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
inconclusive_prob[,index]=0
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob[,index]=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
inconclusive_prob[,index]=0
go_cutoff[index]=temp$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
}
n_overlap=n_overlap[-1]
n_unsatisfied=n_unsatisfied[-1]
####plot figure
if(plot.prob==TRUE){
for(j in 1:length(RR)){
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=xlabtitle,
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 HR=",round(RR[j],digits=3)),xpd=T,adj=0.5,cex=0.8)
if(length(n_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(length(n_unsatisfied)!=0){
text(nmin,115,paste0('Warning: We could not find cutoffs to satisfy your both GO and NOGO criterions.'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(nmin,110,paste0('Please check and modify your desicion rule!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
}
if(plot.cutoff==TRUE){
p.obs_go=go_cutoff
p.obs_nogo=nogo_cutoff
P_go=p.obs_go
P_nogo=p.obs_nogo
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
ylim_max=max(c(P_go,P_nogo,CT1.go,CT2.go,CT1.nogo,CT2.nogo),na.rm=TRUE)+0.1
ylim_min=min(c(P_go,P_nogo,CT1.go,CT2.go,CT1.nogo,CT2.nogo),na.rm=TRUE)-0.1
plot(NA,NA,xlab=xlabtitle,ylab="Estimated hazard ratio",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,0.1),CT1.go,CT2.go,CT1.nogo,CT2.nogo),2),labels=T)
box()
lines(nseq,P_nogo,col=rgb(0.9,0,0),lwd=2)
lines(nseq,P_go,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)
#text((nmin+nmax)/2,ylim_max+0.075,paste0("True HR=",round(RR[j],digits=3)),xpd=T,adj=0.5,cex=0.8)
if(length(n_overlap)!=0){
text(nmin,ylim_max+0.05,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+0.025,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(length(n_unsatisfied)!=0){
text(nmin,ylim_max+0.05,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(nmin,ylim_max+0.025,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(n_unsatisfied,n_overlap))
}
Interim_TAS<-function(interim_n=c(50,100,150),num_interim=3,
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'),
task=c('Futility','Superiority','Futility and superiority'),
logic.go=c('and','or','and'),
logic.nogo=c('and','or','or'),
seed.num=369,nsim_IA=10000,stop.criterion=10^-3,
eventinput=TRUE,
maturity=0.7,
alloc.ratio = 1,
prior.mean = 1,prior.sd=1,
mean=c(0.25),
nsim=10000){
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<-TAS_Survival_Cutoff(eventinput=TRUE,nevents=interim_n[i],npatients=interim_n[i],
maturity=maturity,
alloc.ratio = alloc.ratio,
prior.mean = prior.mean,prior.sd = prior.sd,
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=CT1.nogo[i],
false.nogo.CT2=false.nogo.CT2[i], FNGR.CT2=FNGR.CT2[i],
method=method,direction=direction[i],
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
###TAS####
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)
###TAS####
precision1=alloc.ratio/(1+alloc.ratio)
precision2=1-precision1
sd=sqrt(1/precision1+1/precision2)
sim_data<-matrix(NA,nrow=nsim_IA,ncol=max(interim_n))
for(k in 1:max(interim_n)){
sim_data[,k]<- rnorm(nsim_IA,mean=log(mean[meanindex]),sd=sd)
}
cum_sim_data_temp<-apply(sim_data,1,cumsum)
cum_sim_data<-t(exp(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 HR','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_TAS(interim_n = c(66,131,197),CT1.go=c(1,0.6,1),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),method='Frequentist',direction=c(rep('Less',2),'Greater'),task=c('Futility','Superiority','Superiority'),mean=c(0.25,2))
#
# Fix_SS_TAS_Survival_Prob(eventinput=FALSE,nevents=170,
# npatients=180,maturity=0.4,
# alloc.ratio=1,
# CT1.go=0.65,
# false.go.CT1=TRUE,FGR.CT1=0.7,
# CT1.nogo=0.65,
# false.nogo.CT1=TRUE,FNGR.CT1=0.1,
# CT2.go=10/12,
# false.go.CT2=FALSE, FGR.CT2=0.2,
# CT2.nogo=10/12,
# false.nogo.CT2=FALSE, FNGR.CT2=0.2,
# RR=c(0.933,0.483),overlap.option='GO',plot.figure=TRUE,
# method='Frequentist',direction='Less',prior.mean=1,prior.sd=1,
# 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_TAS_Survival_Prob TAS_Survival_Cutoff
library(shiny)
TAS_Survival_Cutoff<-function(eventinput=TRUE,nevents=126,
npatients=180,maturity=0.7,
alloc.ratio=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='Bayesian',direction='Greater',
prior.mean=1,prior.sd=1,
logic.go='and',
logic.nogo='or'){
if(eventinput==TRUE){n=nevents}else{n=npatients*maturity}
n1=n*alloc.ratio/(1+alloc.ratio)
n2=n-n1
sd=sqrt(1/n1+1/n2)
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(method=='Frequentist'){
if(direction=='Less'){
if(false.go.CT1==TRUE){
est1.go=exp(qnorm(FGR.CT1,mean=log(CT1.go),sd=sd))
}
if(false.go.CT2==TRUE){
est2.go=exp(qnorm(FGR.CT2,mean=log(CT2.go),sd=sd))
}
if(false.nogo.CT1==TRUE){
est1.nogo=exp(qnorm(1-FNGR.CT1,mean=log(CT1.nogo),sd=sd))
}
if(false.nogo.CT2==TRUE){
est2.nogo=exp(qnorm(1-FNGR.CT2,mean=log(CT2.nogo),sd=sd))
}
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(direction=='Greater'){
if(false.go.CT1==TRUE){
est1.go=exp(qnorm(1-FGR.CT1,mean=log(CT1.go),sd=sd))
}
if(false.go.CT2==TRUE){
est2.go=exp(qnorm(1-FGR.CT2,mean=log(CT2.go),sd=sd))
}
if(false.nogo.CT1==TRUE){
est1.nogo=exp(qnorm(FNGR.CT1,mean=log(CT1.nogo),sd=sd))
}
if(false.nogo.CT2==TRUE){
est2.nogo=exp(qnorm(FNGR.CT2,mean=log(CT2.nogo),sd=sd))
}
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(method=='Bayesian'){
temp<-SAN_Normal_Cutoff(n=n,prior.mean = prior.mean,prior.sd=prior.sd,sd=sd*sqrt(n),
CT1.go=log(CT1.go),
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo=log(CT1.nogo),
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=log(CT2.go),
false.go.CT2 = false.go.CT2, FGR.CT2=FGR.CT2,
CT2.nogo=log(CT2.nogo),
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method='Bayesian',direction=direction,
fix.var=TRUE,noninfo=TRUE,
logic.go=logic.go,logic.nogo=logic.nogo)
return(list(cutoff=exp(temp$cutoff),flag=temp$flag,overlap=temp$overlap))
}
}
#####################################################
Fix_SS_TAS_Survival_Prob<-function(eventinput=TRUE,nevents=126,
npatients=180,maturity=0.7,
alloc.ratio=1,
CT1.go=10/14.6,
false.go.CT1=TRUE,FGR.CT1=0.1,
CT1.nogo=10/14.6,
false.nogo.CT1=TRUE,FNGR.CT1=0.1,
CT2.go=10/12,
false.go.CT2=TRUE, FGR.CT2=0.2,
CT2.nogo=10/12,
false.nogo.CT2=FALSE, FNGR.CT2=0.2,
RR=c(0.933,0.483),overlap.option='GO',plot.figure=TRUE,
method='Bayesian',direction='Less',prior.mean=1,prior.sd=1,
logic.go='and',logic.nogo='or'){
if(eventinput==TRUE){n=nevents}else{n=npatients*maturity}
n1=n*alloc.ratio/(1+alloc.ratio)
n2=n-n1
sd=sqrt(1/n1+1/n2)
go_prob<-rep(NA,length(RR))
nogo_prob<-rep(NA,length(RR))
inconclusive_prob<-rep(NA,length(RR))
index=1
unsatisfied.flag=0
overlap.flag=0
temp=TAS_Survival_Cutoff(eventinput=eventinput,nevents=nevents,
npatients=npatients,maturity=maturity,
alloc.ratio = alloc.ratio,
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,
prior.mean=prior.mean,prior.sd=prior.sd,
logic.go = logic.go,logic.nogo=logic.nogo)
###TAS####
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(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
nogo_prob=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
nogo_prob=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
nogo_prob=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
go_prob=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(RR))
go_prob=rep(NA,length(RR))
inconclusive_prob=rep(NA,length(RR))
}
}
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
nogo_prob=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
nogo_prob=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
nogo_prob=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sd)
inconclusive_prob=1-go_prob-nogo_prob
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
nogo_prob=rep(NA,length(RR))
go_prob=rep(NA,length(RR))
inconclusive_prob=rep(NA,length(RR))
}
}
##########################################################################################
if(plot.figure==TRUE){
GOdirectsymbol=NA
NOGOdirectsymbol=NA
RRseq=seq(min(RR),max(RR),0.05)
go_prob_plot<-rep(NA,length(RRseq))
nogo_prob_plot<-rep(NA,length(RRseq))
inconclusive_prob_plot<-rep(NA,length(RRseq))
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
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_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
}
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
}
}
delta=RRseq
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("True ",HR,sep="")),
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=range(delta),
ylim=c(0,100),type="n",axes=F)
axis(1, at=seq(min(RR),max(RR),0.05), labels=T)
axis(2, at=seq(0,100,10),labels=T)
box()
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(RR),140,bquote(Number~of~events==~.(nevents)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
}
if(eventinput==FALSE){
text(min(RR),140,bquote(Number~of~patients==~.(npatients)~';'~Maturity==~.(maturity)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
}
if(direction=='Greater'){
if(overlap.flag==0&unsatisfied.flag==0){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
text(min(RR),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"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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(min(RR),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"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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(min(RR),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(min(RR),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(min(RR),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(direction=='Less'){
if(overlap.flag==0&unsatisfied.flag==0){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
text(min(RR),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"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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(min(RR),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"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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(min(RR),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(min(RR),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(min(RR),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){
GOdirectsymbol=NA
NOGOdirectsymbol=NA
RRseq=seq(min(RR),max(RR),0.05)
go_prob_plot<-rep(NA,length(RRseq))
nogo_prob_plot<-rep(NA,length(RRseq))
inconclusive_prob_plot<-rep(NA,length(RRseq))
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
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_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
nogo_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
}
}
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob_plot=pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[1]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob_plot=pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
nogo_prob_plot=1-pnorm(log(temp$cutoff[2]),mean=log(RRseq),sd=sd)
inconclusive_prob_plot=1-go_prob_plot-nogo_prob_plot
overlap.flag=1
}
if(all(temp$flag==0)==FALSE){
unsatisfied.flag=1
}
}
delta=RRseq
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("True ",HR,sep="")),
ylab="Prob of GO/ NOGO/ Inconclusive (%)",xlim=range(delta),
ylim=c(0,100),type="n",axes=F)
axis(1, at=seq(min(RR),max(RR),0.05), labels=T)
axis(2, at=seq(0,100,10),labels=T)
box()
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(RR),140,bquote(Number~of~events==~.(nevents)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
}
if(eventinput==FALSE){
text(min(RR),140,bquote(Number~of~patients==~.(npatients)~';'~Maturity==~.(maturity)),xpd=T,adj=0,cex=0.8,col=rgb(0, 0,0))
}
if(direction=='Greater'){
if(overlap.flag==0&unsatisfied.flag==0){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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(min(RR),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"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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(min(RR),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(min(RR),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(min(RR),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(direction=='Less'){
if(overlap.flag==0&unsatisfied.flag==0){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[1],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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(min(RR),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"){
text(min(RR),130,bquote(GO~symbol("\336")~Observed~hazard~ratio~symbol("\243")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0, 0.7,0))
text(min(RR),120,bquote(NOGO~symbol("\336")~Observed~hazard~ratio~symbol("\263")~.(round(temp$cutoff[2],digits=3))),xpd=T,adj=0,cex=0.8,col=rgb(0.9, 0,0))
#text(min(RR),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(min(RR),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(min(RR),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(min(RR),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$cutoff,true_cutoff=c(true_go_cutoff,true_nogo_cutoff))
)
}
Vary_SS_TAS_Survival_Prob<-function(eventinput=TRUE,
neventsmin=70,neventsmax=350,
npatientsmin=100,npatientsmax=500,
maturity=0.7,
alloc.ratio=1,
CT1.go=0.25,
false.go.CT1=TRUE,FGR.CT1=0.25,
CT1.nogo=0.25,
false.nogo.CT1=TRUE,FNGR.CT1=0.25,
CT2.go=0.3,
false.go.CT2=TRUE, FGR.CT2=0.5,
CT2.nogo=0.3,
false.nogo.CT2=TRUE, FNGR.CT2=0.5,
RR=c(0.25),overlap.option='GO',
plot.cutoff=TRUE,plot.prob=TRUE,
method='Bayesian',direction='Greater',
prior.mean=1,prior.sd=1,
logic.go='and',logic.nogo='or'){
if(eventinput==TRUE){nmin=neventsmin
nmax=neventsmax
nseq=unique(round(c(seq(nmin,(nmax+nmin)/2,length=6)[-6],seq((nmax+nmin)/2,nmax,length=6))))
n1=nseq*alloc.ratio/(1+alloc.ratio)
n2=nseq*1/(1+alloc.ratio)
sdseq=sqrt(1/n1+1/n2)
xlabtitle=paste0('Number of events')}else{
nmin=npatientsmin
nmax=npatientsmax
nseq=seq(nmin,nmax,by=1)
n1=nseq*maturity*alloc.ratio/(1+alloc.ratio)
n2=nseq*maturity*1/(1+alloc.ratio)
sdseq=sqrt(1/n1+1/n2)
xlabtitle=paste0('Number of patients (Maturity= ',maturity,')')
}
go_prob<-matrix(NA,ncol=length(nseq),nrow=length(RR))
nogo_prob<-matrix(NA,ncol=length(nseq),nrow=length(RR))
inconclusive_prob<-matrix(NA,ncol=length(nseq),nrow=length(RR))
go_cutoff<-rep(NA,length(nseq))
nogo_cutoff<-rep(NA,length(nseq))
index=1
n_unsatisfied=NA
n_overlap=NA
for(i in nseq){
temp=TAS_Survival_Cutoff(eventinput = eventinput,
npatients = i,maturity = maturity,
nevents=i,alloc.ratio = alloc.ratio,
CT1.go=CT1.go,
false.go.CT1 = false.go.CT1, FGR.CT1=FGR.CT1,
CT1.nogo=CT1.nogo,
false.nogo.CT1 = false.nogo.CT1, FNGR.CT1 = FNGR.CT1,
CT2.go=CT2.go,
false.go.CT2 = false.go.CT2,FGR.CT2=FGR.CT2,
CT2.nogo=CT2.nogo,
false.nogo.CT2 = false.nogo.CT2, FNGR.CT2 = FNGR.CT2,
method=method,direction=direction,
prior.mean=prior.mean,prior.sd=prior.sd,
logic.go=logic.go,logic.nogo=logic.nogo)
if(direction=='Less'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob[,index]=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
inconclusive_prob[,index]=1-go_prob[,index]-nogo_prob[,index]
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="GO"){
go_prob[,index]=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
inconclusive_prob[,index]=0
go_cutoff[index]=temp$cutoff[1]
nogo_cutoff[index]=temp$cutoff[2]
n_overlap=c(n_overlap,i)
}
if(all(temp$flag==0)&temp$overlap==1&overlap.option=="NOGO"){
go_prob[,index]=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=1-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
inconclusive_prob[,index]=0
go_cutoff[index]=temp$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)
}
}
if(direction=='Greater'){
if(all(temp$flag==0)&temp$overlap==0){
go_prob[,index]=1-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
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-pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=pnorm(log(temp$cutoff[1]),mean=log(RR),sd=sdseq[index])
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-pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
nogo_prob[,index]=pnorm(log(temp$cutoff[2]),mean=log(RR),sd=sdseq[index])
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
}
n_overlap=n_overlap[-1]
n_unsatisfied=n_unsatisfied[-1]
####plot figure
if(plot.prob==TRUE){
for(j in 1:length(RR)){
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=xlabtitle,
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 HR=",round(RR[j],digits=3)),xpd=T,adj=0.5,cex=0.8)
if(length(n_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(length(n_unsatisfied)!=0){
text(nmin,115,paste0('Warning: We could not find cutoffs to satisfy your both GO and NOGO criterions.'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
text(nmin,110,paste0('Please check and modify your desicion rule!'),xpd=T,adj=0,cex=0.5,col=rgb(0.9,0,0))
}
}
}
if(plot.cutoff==TRUE){
p.obs_go=go_cutoff
p.obs_nogo=nogo_cutoff
P_go=p.obs_go
P_nogo=p.obs_nogo
par(mfrow=c(1,1),pty="m",bty="l",cex=1.4)
ylim_max=max(c(P_go,P_nogo,CT1.go,CT2.go,CT1.nogo,CT2.nogo),na.rm=TRUE)+0.1
ylim_min=min(c(P_go,P_nogo,CT1.go,CT2.go,CT1.nogo,CT2.nogo),na.rm=TRUE)-0.1
plot(NA,NA,xlab=xlabtitle,ylab="Estimated hazard ratio",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,0.1),CT1.go,CT2.go,CT1.nogo,CT2.nogo),2),labels=T)
box()
lines(nseq,P_nogo,col=rgb(0.9,0,0),lwd=2)
lines(nseq,P_go,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)
#text((nmin+nmax)/2,ylim_max+0.075,paste0("True HR=",round(RR[j],digits=3)),xpd=T,adj=0.5,cex=0.8)
if(length(n_overlap)!=0){
text(nmin,ylim_max+0.05,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+0.025,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(length(n_unsatisfied)!=0){
text(nmin,ylim_max+0.05,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(nmin,ylim_max+0.025,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(n_unsatisfied,n_overlap))
}
Interim_TAS<-function(interim_n=c(50,100,150),num_interim=3,
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'),
task=c('Futility','Superiority','Futility and superiority'),
logic.go=c('and','or','and'),
logic.nogo=c('and','or','or'),
seed.num=369,nsim_IA=10000,stop.criterion=10^-3,
eventinput=TRUE,
maturity=0.7,
alloc.ratio = 1,
prior.mean = 1,prior.sd=1,
mean=c(0.25),
nsim=10000){
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<-TAS_Survival_Cutoff(eventinput=TRUE,nevents=interim_n[i],npatients=interim_n[i],
maturity=maturity,
alloc.ratio = alloc.ratio,
prior.mean = prior.mean,prior.sd = prior.sd,
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=CT1.nogo[i],
false.nogo.CT2=false.nogo.CT2[i], FNGR.CT2=FNGR.CT2[i],
method=method,direction=direction[i],
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
###TAS####
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)
###TAS####
precision1=alloc.ratio/(1+alloc.ratio)
precision2=1-precision1
sd=sqrt(1/precision1+1/precision2)
sim_data<-matrix(NA,nrow=nsim_IA,ncol=max(interim_n))
for(k in 1:max(interim_n)){
sim_data[,k]<- rnorm(nsim_IA,mean=log(mean[meanindex]),sd=sd)
}
cum_sim_data_temp<-apply(sim_data,1,cumsum)
cum_sim_data<-t(exp(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 HR','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_TAS(interim_n = c(66,131,197),CT1.go=c(1,0.6,1),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),method='Frequentist',direction=c(rep('Less',2),'Greater'),task=c('Futility','Superiority','Superiority'),mean=c(0.25,2))
#
# Fix_SS_TAS_Survival_Prob(eventinput=FALSE,nevents=170,
# npatients=180,maturity=0.4,
# alloc.ratio=1,
# CT1.go=0.65,
# false.go.CT1=TRUE,FGR.CT1=0.7,
# CT1.nogo=0.65,
# false.nogo.CT1=TRUE,FNGR.CT1=0.1,
# CT2.go=10/12,
# false.go.CT2=FALSE, FGR.CT2=0.2,
# CT2.nogo=10/12,
# false.nogo.CT2=FALSE, FNGR.CT2=0.2,
# RR=c(0.933,0.483),overlap.option='GO',plot.figure=TRUE,
# method='Frequentist',direction='Less',prior.mean=1,prior.sd=1,
# logic.go='and',logic.nogo='or')
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