Peter Dalgaard wrote: > Kevin E. Thorpe wrote: >> Dear List: >> >> I have a data frame prepared in the couting process style for including >> a binary time-dependent covariate. The first few rows look like this. >> >> PtNo Start End Status Imp >> 1 1 0 608.0 0 0 >> 2 2 0 513.0 0 0 >> 3 2 513 887.0 0 1 >> 4 3 0 57.0 0 0 >> 5 3 57 604.0 0 1 >> 6 4 0 150.0 1 0 >> >> >> The outcome is mortality and the covariate is for an implantable >> defibrillator, so it is expected that the implant would reduce the >> risk of death. The results of fitting coxph (survival package) are: >> >> Call: >> coxph(formula = Surv(Start, End, Status) ~ Imp, data = nina.excl) >> >> >> coef exp(coef) se(coef) z p >> Imp 0.163 1.18 0.485 0.337 0.74 >> >> Likelihood ratio test=0.11 on 1 df, p=0.738 n= 335 >> >> Since this was unexpected, I created a non-counting process data >> frame with an indicator variable representing received an implant >> or not. Here are the results: >> >> Call: >> coxph(formula = Surv(Days, Dead) ~ Implant, data = nina.excl0) >> >> >> coef exp(coef) se(coef) z p >> Implant -1.77 0.171 0.426 -4.15 3.3e-05 >> >> Likelihood ratio test=19.1 on 1 df, p=1.21e-05 n= 197 >> >> I found this degree of discrepancy surprising, especially the point >> estimate of the coefficient. I have verified the data frames are >> set up correctly. >> >> Here is what I have tried to understand what is going on. >> >> I tried fitting models adjusted for other covariates that I have in >> the data frame. This did not appreciably affect the coefficients >> for the implant variable. >> >> I ran cox.zph on the two models shown above and plotted the results. >> In both cases, the point estimate of Beta(t) is sort of parabolic >> in that the curves are monotonically increasing to a local maximum >> after which they are monotonically decreasing (the CIs are a bit >> more wiggly). >> >> I would interpret this to mean that the effect of implant is probably >> time-dependent. If so, how do I actually get a "proper" estimate of >> beta(t) for a variable like this? >> >> Are there some other things I should look at to understand what's >> going on? >> >> > If you want to play with time-dependent regression coefficients have a > look at the timereg package and the book that it supports. > > However, first you need to consider the possibility of selection effects > that can take place even with non-varying effects. In the case at hand I > would suspect a bias created by the fact that you don't implant devices > into people who are already dead. >
Thanks. The point in your last paragraph did cross my mind too. -- Kevin E. Thorpe Biostatistician/Trialist, Knowledge Translation Program Assistant Professor, Department of Public Health Sciences Faculty of Medicine, University of Toronto email: [EMAIL PROTECTED] Tel: 416.864.5776 Fax: 416.864.6057 ______________________________________________ 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.
