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. > Here is my sessionInfo. > R version 2.5.0 (2007-04-23) > i686-pc-linux-gnu > > locale: > LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.UTF-8;LC_MONETARY=en_US.UTF-8;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTIFICATION=C > > attached base packages: > [1] "splines" "stats" "graphics" "grDevices" "utils" "datasets" > [7] "methods" "base" > > other attached packages: > cmprsk survival > "2.1-7" "2.31" > > > -- O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ 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.
