search for: censboot

...=T) I've used names(modz) but can't see anything that will let me extract the Z scores for each coefficient or the standard errors in the same way one can use coef (modz). I need to get either the se or Zscore as I am hoping to be able to use the Zscore within a statistic function used in censboot. I wish to find the proportion of times each of the variables in the cox model are significant out of the R=1000 bootstrap resamples. My current function for us with the bootstrap only gives me the coefficients: - func=function(kidneyT,indices) { kidneyT=kidneyT[indices,] modz=coxph(Surv(TSURV,...

Dear all, I run the example of "censboot" contained in "boot" package. But, I can't find the confidence interval of the resulted "censboot" object. Any idea ? > aml.fun <- function(data) { + surv <- survfit(Surv(time, cens)~group, data=data) + out <- NULL + st <-...

...oxph(Surv(filename$time,filename$cens==1) ~ filename$x1+filename$x2+filename$x3a+filename$x3b+filename$x3c)) + } > > > ## Calculate cox's regression coefficients' standard error and bias using the Ordinary Bootstrap > > set.seed(1234)#set the random start > cox.ord <-censboot(data=filename,statistic=beta.fun,R=r,F.surv=data.surv, G.surv=data.cens,cox=data.cox,sim="ordinary") Warning messages: 1: Loglik converged before variable 3,4,5 ; beta may be infinite. in: fitter(X, Y, strats, offset, init, control, weights = weights, 2: Loglik converged before varia...

Dear mailing list, I tried to run the example for the conditional bootstap written in the help file of censboot. I got the following result: STRATIFIED CONDITIONAL BOOTSTRAP FOR CENSORED DATA Call: censboot(data = aml, statistic = aml.fun, R = 499, F.surv = aml.s1, G.surv = aml.s2, strata = aml$group, sim = "cond") Bootstrap Statistics : original bias std. error t1* 31...

Hello, I have been trying to use the censored bootstrap function 'censboot' from the library boot, with the function 'survfit' from the library survival5. My code is: surv.fun <-function(data) { sur<-survfit(Surv(data[,1],data[,2]), data=data) } c.boot<-censboot(m,surv.fun,R=99) m is my data set I get the error message: Error in eval(attr(form...

...)~thickness, + data=mel) > mel.surv <- survfit(mel.cox) > mel.cens <- survfit(Surv(time-0.001*(status==1),status!=1)~1, + data=mel) > mel.fun <- function(d) { + cox <- coxph(Surv(time, status==1)~thickness, + data=d) + cox$coefficients} > mel.boot.con <- censboot(mel, mel.fun, R=999, sim="cond", + F.surv=mel.surv, G.surv=mel.cens, + cox=mel.cox, index=c(1,8)) > mel.boot.con CONDITIONAL BOOTSTRAP FOR CENSORED DATA Call: censboot(data = mel, statistic = mel.fun, R = 999, F.surv = mel.surv, G.surv = mel.cens, sim = "cond&q...

...e) { 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) #}...

...)~thickness, + data=mel) > mel.surv <- survfit(mel.cox) > mel.cens <- survfit(Surv(time-0.001*(status==1),status!=1)~1, + data=mel) > mel.fun <- function(d) { + cox <- coxph(Surv(time, status==1)~thickness, + data=d) + cox$coefficients} > mel.boot.con <- censboot(mel, mel.fun, R=999, sim="cond", + F.surv=mel.surv, G.surv=mel.cens, + cox=mel.cox, index=c(1,8)) > mel.boot.con CONDITIONAL BOOTSTRAP FOR CENSORED DATA Call: censboot(data = mel, statistic = mel.fun, R = 999, F.surv = mel.surv, G.surv = mel.cens, sim = "cond&qu...

...)~thickness, + data=mel) > mel.surv <- survfit(mel.cox) > mel.cens <- survfit(Surv(time-0.001*(status==1),status!=1)~1, + data=mel) > mel.fun <- function(d) { + cox <- coxph(Surv(time, status==1)~thickness, + data=d) + cox$coefficients} > mel.boot.con <- censboot(mel, mel.fun, R=999, sim="cond", + F.surv=mel.surv, G.surv=mel.cens, + cox=mel.cox, index=c(1,8)) > mel.boot.con CONDITIONAL BOOTSTRAP FOR CENSORED DATA Call: censboot(data = mel, statistic = mel.fun, R = 999, F.surv = mel.surv, G.surv = mel.cens, sim = "cond&qu...

...)~thickness, + data=mel) > mel.surv <- survfit(mel.cox) > mel.cens <- survfit(Surv(time-0.001*(status==1),status!=1)~1, + data=mel) > mel.fun <- function(d) { + cox <- coxph(Surv(time, status==1)~thickness, + data=d) + cox$coefficients} > mel.boot.con <- censboot(mel, mel.fun, R=999, sim="cond", + F.surv=mel.surv, G.surv=mel.cens, + cox=mel.cox, index=c(1,8)) > mel.boot.con CONDITIONAL BOOTSTRAP FOR CENSORED DATA Call: censboot(data = mel, statistic = mel.fun, R = 999, F.surv = mel.surv, G.surv = mel.cens, sim = "cond&qu...

Dear R-list, I am wondering how to perform a bootstrap in R for the weighted time dependent Cox model? (Andersen?Gill format, with multiple observations from each patients) to obtain the bootstrap standard error of the treatment effect. Below is an example dataset. Would 'censboot' be appropriate to use in this context? Any suggestions/references/direction to R-package will be highly appreciated. Thanks Ehsan ########################### > dataset = read.csv("http://stat.ubc.ca/~e.karim/dataset2.csv") > head(dataset) # (tx = treatment, weight = IPTW)...