Dear Prof Frank,
I tried to simulate an example data set as close as possible to my own real
data with the codes below. There are only two covariates, tumor(3 levels)
and ecog(3 levels). "rx" is treatment (4 levels). Validation with the
stratified model (by rx) had a negative R2.. and the R2 under the training
column was so high...?
n<-1000
set.seed(110222)
data<-matrix(rep(0,5000),ncol=5)
data[,1]<-sample(1:3,n,rep=TRUE,prob=c(.32, .30, .38))
for (i in 1:1000){
if (data[i,1]==1) data[i,2]<-sample(1:3,1,prob=c(.76, .18, .06))
if (data[i,1]==2) data[i,2]<-sample(1:3,1,prob=c(.67, .24, .09))
if (data[i,1]==3) data[i,2]<-sample(1:3,1,prob=c(.47, .37, .16))}
for (i in 1:1000){
if (data[i,1]==1) data[i,3]<-sample(1:4,1,prob=c(.70, .19, .03, .08))
if (data[i,1]==2) data[i,3]<-sample(1:4,1,prob=c(.42, .28, .12, .18))
if (data[i,1]==3) data[i,3]<-sample(1:4,1,prob=c(.11, .29, .30, .30))}
for (i in 1:1000){
if (data[i,3]==1)
data[i,4]<-12*rgamma(1000,rate=0.4,shape=1.7)[c(sample(26:975,1,prob=c(rep(1/950,950))))]
if (data[i,3]==2)
data[i,4]<-12*rgamma(1000,rate=0.9,shape=1.7)[c(sample(26:975,1,prob=c(rep(1/950,950))))]
if (data[i,3]==3)
data[i,4]<-12*rgamma(1000,rate=1.2,shape=0.6)[c(sample(26:975,1,prob=c(rep(1/950,950))))]
if (data[i,3]==4)
data[i,4]<-12*rgamma(1000,rate=1.5,shape=0.7)[c(sample(26:975,1,prob=c(rep(1/950,950))))]}
for (i in 1:1000){
if (data[i,3]==1) data[i,5]<-sample(c(0,1),1,prob=c(.53, .47))
if (data[i,3]==2) data[i,5]<-sample(c(0,1),1,prob=c(.17, .83))
if (data[i,3]==3) data[i,5]<-sample(c(0,1),1,prob=c(.05, .95))
if (data[i,3]==4) data[i,5]<-sample(c(0,1),1,prob=c(.06, .94))}
colnames(data)<-c("tumor","ecog","rx","os","censor")
data<-data.frame(data)
attach(data)
library(rms)
surv.obj<-Surv(os,censor)
validate(cph(surv.obj~factor(tumor)+factor(ecog),x=T,y=T,surv=T),method="b",B=100,dxy=T)
validate(cph(surv.obj~factor(tumor)+factor(ecog)+strat(rx),
x=T,y=T,surv=T,time.inc=60),method="b",B=100,dxy=T,u=60)
Best regards,
Vikki
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