
library(survival)  library(boot)    data=NULL  lambda=NULL  result=NULL  pat=rep(1:102,each=1)  trt=rep(c(1,0),51)  status=rep(1,102)  site=rep(1:51, each=2)  nr.datasets=100  seed=2006    beta=log(1/2)    for (i in 1:51) {  lambda[i]=1+((3-1)/50)*(i-1)}  lambda1=rep(lambda, each=2)  dummy=rep(c(exp(beta),1),51)  elf=lambda1*dummy    r=70 #the number of bootstrap replicates    #mymodels=function(dataset.number){  #seed=2006*dataset.number  time=rexp(102, rate=elf)    data=cbind(pat,trt,status,site,time)  data1=data.frame(data)    #### Cox proportional hazard model stratified on site    model1.cox=coxph(Surv(time,status) ~ trt + strata(site), data=data1,   iter.max=500)  model1.surv=survfit(model1.cox)    #### Cox proportional hazard model with dummy covariates for site    model2.cox=coxph(Surv(time,status) ~ trt + factor(site), data=data1,   iter.max=500)  model2.surv=survfit(model2.cox)    beta.fun1 = function(dataname) {  cox = coef(coxph(Surv(dataname$time,dataname$status) ~ 
 dataname$trt+strata(dataname$site)))   }     beta.fun2 = function(dataname) {  cox =coef(coxph(Surv(dataname$time,dataname$status) ~  dataname$trt+factor(dataname$site)))  #cox=cox[1]   }    dataname=data1  ## Calculate Cox's regression coefficients' standard error and bias   using the  ## ordinary Bootstrap    # set.seed(1234)#set the random start  cox.ord1 =   censboot(data=dataname,statistic=beta.fun1,R=r,F.surv=model1.surv,  cox=model1.cox,sim="ordinary")  cox.ord1  cox.ord2 =   censboot(data=dataname,statistic=beta.fun2,R=r,F.surv=model2.surv,  cox=model2.cox,sim="ordinary")   cox.ord2        join=list(model1, model2)  #return(join)  #}    #for (i in 1:2){  #result[i]=mymodels}  

I run the above code but for the following part:

cox.ord2 =   censboot(data=dataname,statistic=beta.fun2,R=r,F.surv=model2.surv,  cox=model2.cox,sim="ordinary")   cox.ord2  

I get an error and says that it did not converge. I am not sure whats wrong in my coding.

I want to calculate the bias. Any help is very much appreciated.

 

Thanks,

Orlando

 

		
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