search for: subcohort

...ax=35 in Prentice function to iter.max=0 should do the trick. But I wanted to hear from the experts (or the author of the program) on this issue. The fact that SE's are identical can be found by the R help example codes of CCH. I'm copying and pasting them. subcoh <- nwtco$in.subcohort selccoh <- with(nwtco, rel==1|subcoh==1) ccoh.data <- nwtco[selccoh,] ccoh.data$subcohort <- subcoh[selccoh] ## central-lab histology ccoh.data$histol <- factor(ccoh.data$histol,labels=c("FH","UH")) ## tumour stage ccoh.data$...

...reate your fit: Assume that your data set has the ususal coxph variables, including time-dependent covariates as multiple observations per subject using (start, stop) style, along with 2 other variables id = a unique identifier per subject case = 0 if the subject is a member of the random subcohort 1 if the subject is a case (an event from outside the subcohort) Then coxph(Surv(time1, time2, status) ~ x1 + x2+ .... + offset(-100*case) + cluster(id), data=mydata) Will fit the case-cohort model. This correctly allows for time-dependent covar...

...le different. Can we exactly use coxph() to repeat cch() using with appropriate configuration in coxph()? Is SAS a better way(PHREG,CASECOH.SAS) to implement time-dependent case-cohort? > summary(fit2.ccP) Call: coxph(formula = Surv(edrel, rel) ~ stage + histol + age + offset(-100 * (1 - subcohort)) + cluster(seqno), data = ccoh.data) n= 1154 coef exp(coef) se(coef) robust se z p stageII 0.7363 2.09 0.1213 0.1699 4.33 1.5e-05 stageIII 0.5976 1.82 0.1233 0.1753 3.41 6.5e-04 stageIV 1.3921 4.02 0.1339 0.2081 6.69 2.2e-11 histolUH 1.5059...

Case cohort function cch() is in survival package. In cch(), the prentice method is implemented like this: Prentice <- function(tenter, texit, cc, id, X, ntot,robust){ eps <- 0.00000001 cens <- as.numeric(cc>0) # Censorship indicators subcoh <- as.numeric(cc<2) # Subcohort indicators ## Calculate Prentice estimate ent2 <- tenter ent2[cc==2] <- texit[cc==2]-eps fit1 <- coxph(Surv(ent2,texit,cens)~X,eps=eps,x=TRUE) ## Calculate Prentice estimate and variance nd <- sum(cens) # Number of failures nc <- sum(subcoh) # Number in su...

Jin Wang had an error. My original note specified a variable that was 1 for subjects NOT in the subcohort, so the correct coxph call is coxph(Surv(edrel, rel) ~ stage + histol + age + offset(-100*(subcohort==0)) + cluster(seqno), data =ccoh.data) This gives the same coefficients as the cch example, along with the infinitesimal jackknife or "robust" variance estimate. Terry Ther...