riskscore<-as.matrix(dat[-i,index0])%*% as.matrix(coefficients)
y1<-survivalROC(Stime=test$time,status=test$status,marker=riskscore,predict.time=1,span = 0.25*(nrow(train))^(-0.20))
y3<-survivalROC(Stime=test$time,status=test$status,marker=riskscore,predict.time=3,span = 0.25*(nrow(train))^(-0.20))
y5<-survivalROC(Stime=test$time,status=test$status,marker=riskscore,predict.time=5,span = 0.25*(nrow(train))^(-0.20))
a<-matrix(data=c(\
plot(y1$FP,y1$TP,type=\Positive Rate\= \Positive Rate\ lines(y3$FP,y3$TP,col=\lines(y5$FP,y5$TP,col=\
legend(\= c(\at 1 year:0.8761\at 3 years:0.7611\at 5 years:0.7611\abline(0,1)
a<-matrix(0,30,3) for (c in 1:30){
i<-sample(1:77,52) train<-data[i,] test<-data[-i,]
mod.brea<-coxph(Surv(time,status)~.,data=train)
train_data<-train[,c(1,2,which(summary(mod.brea)$coefficients[,5]<=0.1)+2)] tset_data<-test[,c(1,2,which(summary(mod.brea)$coefficients[,5]<=0.1)+2)] mod.brea1<-coxph(Surv(time,status)~.,data=train_data) names(coef(mod.brea1))
index0<-numeric(length(coef(mod.brea1))) coefficients<-coef(mod.brea1)
name<-gsub(\ for(j in 1:length(index0)){
index0[j]<-which(names(dat)==name[j]) }
riskscore<-as.matrix(dat[-i,index0])%*% as.matrix(coefficients)
y1<-survivalROC(Stime=test$time,status=test$status,marker=riskscore,predict.time=1,span = 0.25*(nrow(train))^(-0.20))
y3<-survivalROC(Stime=test$time,status=test$status,marker=riskscore,predict.time=
3,span = 0.25*(nrow(train))^(-0.20))
y5<-survivalROC(Stime=test$time,status=test$status,marker=riskscore,predict.time=5,span = 0.25*(nrow(train))^(-0.20))
a[c,]<-c(y1$AUC,y3$AUC,y5$AUC) }