same subject id has to be multiple in mutiple times like following format, Multiple records per id not allowed in cch() so it's difficult to use cch() for time dependent covariate. Maybe coxph() is alternative, but seems difficult because coxph() and cch() return different estimate for same data "nwtco" even without time-dependent covariates.
id start end event 1 3 4 1 4 5 1 5 6 1 2 2 3 2 3 4 2 4 5 2 5 6 1 I use the time-dependent covariates data "Rossi" in http://socserv.mcmaster.ca/jfox/Books/Companion/appendix-cox-regression.pdf I rebuild new case cohort data with time dependent variable based on Rossi data sc<-sample(c(TRUE,FALSE,FALSE,FALSE,FALSE,FALSE), 432, replace = TRUE) str(Rossi) Rossi1<-cbind(Rossi,sc) Rossi2<-cbind(seqno,Rossi1) subcoh1 <- Rossi2$sc selccoh1 <- with(Rossi2, arrest.time==1|subcoh1==1) ccoh1.data <- Rossi2[selccoh1,] ccoh1.data$subcohort <- subcoh1[selccoh1] str(ccoh1.data) ccoh1.data.fold <- fold(ccoh1.data, time='week', event='arrest', cov=12:63, cov.names='employed') str(ccoh1.data.fold) ccoh1.data.fold$sc<-as.logical(ccoh1.data.fold$sc) ccoh1.data.fold$subcohort<-as.logical(ccoh1.data.fold$subcohort) fit1.allison.2 <- cch(Surv(start, stop, arrest.time) ~ fin + age + race + wexp + mar + paro + prio + employed, data=ccoh1.data.fold,subcoh=~subcohort,id=~seqno,cohort.size=19809) history(1000) > fit1.allison.2 <- cch(Surv(start, stop, arrest.time) ~ + fin + age + race + wexp + mar + paro + prio + employed, + data=ccoh1.data.fold,subcoh=~subcohort,id=~seqno,cohort.size=19809) Error in cch(Surv(start, stop, arrest.time) ~ fin + age + race + wexp + : Multiple records per id not allowed ======================================================================= 2008/6/12 Jin Wang <[EMAIL PROTECTED]>: > I tried your alternative method on the example in cch() description manual. > The example data "nwtco" has not time-dependent covariates yet. I test cch() > and coxph() on the same data. But the estimation result is different. I > don't know if I did anything wrong. > > 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 > fit.ccSP <- cch(Surv(edrel, rel) ~ stage + histol + age, data =ccoh.data, > subcoh = ~subcohort, id=~seqno, cohort.size=4028, method="SelfPren") > fit2.ccP <- coxph(Surv(edrel, rel) ~ stage + histol + age + > offset(-100*subcohort)+cluster(seqno),data =ccoh.data) > > > > fit2.ccP > Call: > coxph(formula = Surv(edrel, rel) ~ stage + histol + age + offset(-100 * > subcohort) + cluster(seqno), data = ccoh.data) > > coef exp(coef) se(coef) robust se z p > stageII -0.1245 0.883 0.1236 0.1371 -0.908 0.3600 > stageIII 0.0193 1.020 0.1252 0.1517 0.127 0.9000 > stageIV 0.2997 1.350 0.1370 0.1509 1.986 0.0470 > histolUH 0.3518 1.422 0.0920 0.1092 3.223 0.0013 > age -0.0281 0.972 0.0144 0.0168 -1.678 0.0930 > Likelihood ratio test=34.5 on 5 df, p=1.89e-06 n= 1154 > > > > summary(fit.ccSP) > Case-cohort analysis,x$method, SelfPrentice > with subcohort of 668 from cohort of 4028 > > Call: cch(formula = Surv(edrel, rel) ~ stage + histol + age, data = > ccoh.data, > subcoh = ~subcohort, id = ~seqno, cohort.size = 4028, method = > "SelfPren") > > Coefficients: > Coef HR (95% CI) p > stageII 0.736 2.088 1.491 2.925 0.000 > stageIII 0.597 1.818 1.285 2.571 0.001 > stageIV 1.392 4.021 2.670 6.057 0.000 > histolUH 1.506 4.507 3.274 6.203 0.000 > age 0.043 1.044 0.996 1.095 0.069 > > > 2008/6/12 Terry Therneau <[EMAIL PROTECTED]>: > > ----- 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 allowed >> I know coxph() in Survival package can estimate time dependent covariates. >> ------ end inclusion ----------------------------------------------- >> >> The cch function was added to the package by Breslow and Lumley, neither >> of >> which appears to be monitoring the list lately. Since it claims to >> impliment >> the methods in Li and Therneau, and I don't know the cch code, let me >> suggest an >> alternate way to create 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 >> covariates. It corresponds to the "Self" method of cch. >> Why -100? It causes the case to have a relative weight of approx 0 in a >> particular weighted mean; exp(-100) is small enough and doesn't cause >> trouble >> for the exp function. >> >> Terry Therneau >> >> >> > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
