On Fri, 13 Aug 2004, Mayeul KAUFFMANN wrote: > > > you can always use parametric models and a > > full likelihood (but you may have to program them yourself). > > Prof Brian Ripley > > I started trying this but I could not make the counting process notation > work on this. > (Andersen, P.K. and Gill, R.D. (1982). Cox's regression model for counting > processes: A large sample study. Ann. stat. 10 , 1100-1120). > I think it is only (currently) available for Cox model with R. > > survreg(Surv(start, stop,status)~ x1,data=data ) > Error in survreg(Surv(start, stop, status) ~ x1, data = data) : > Invalid survival type >
Yes. survreg() fits parametric accelerated failure models, not proportional hazards models, and time-varying covariates present more difficulties for accelerated failure models. However, you were concerned about bias because the covariates at event times are not representative. If this is the case, the bias will still be present in a parametric proportional hazards model, and you do not have proportional hazards. The Cox model gives consistent estimates whenever a parametric proportional hazards model does, and the loss of efficiency is typically very small. To recover inter-event information, and especially if it is needed to remove bias, you probably need a joint model for the event process and the covariate process. -thomas ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html