Kevin E. Thorpe wrote: > 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. >
I thought about this some more, and I'm not sure that possibility is "to blame." In my time-dependent model, I don't think I'm doing anything different than is done for transplant in the Stanford Heart Study (the often used example for this kind of time-dependent covariate). As in my case, you would not transplant a dead patient. So, I remain puzzled as to why my model is misbehaving. -- 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.
