search for: histol

...39;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$stage <- factor(ccoh.data$stage,labels=c("I","II","III","IV")) ccoh.data$age <- ccoh.data$age/12 # Age in years...

----- begin included message In case cohort study, we can fit proportional hazard regression model to case-cohort data. In R, the function is cch() in Survival package Now I am working on case cohort analysis with time dependent covariates using cch() of "Survival" R package. I wonder if cch() provide this utility or not? The cch() manual does not say if time dependent covariate is

...er and upper CI and P-value are a little 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...

...gery : Factor w/ 2 levels "1","2": 1 1 2 1 2 1 1 1 1 1 ... $ ptnm.t : Factor w/ 5 levels "0","1","2","3",..: 3 3 5 1 2 4 2 1 1 5 ... $ grade : Factor w/ 3 levels "1","2","3": 2 2 3 2 2 3 2 1 1 3 ... $ histol : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ... $ postbca : Factor w/ 2 levels "0","1": 2 1 2 2 2 NA 2 1 1 1 ... $ postcyst : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ... $ chemo : Factor w/ 2 levels "0",&qu...

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 Therneau