On Feb 13, 2015, at 7:49 AM, Steve Bellan wrote: > Thanks David. I’m not sure I completely follow. Are you referring to sandwich > type estimators like that implemented by using cluster() instead of a frailty > term? > Could you please also clarify your last sentence?
I wasn't suggesting a sandwich estimator. I was imagining you would sample from a population and that some of your sample strata would have zero elements. I would expect that your boot function would trap that event and return an appropriate indicator. The bootstrap in my imagination wouldn't use p-values as the result but rather would report a high log hazard. -- David. > > On Feb 12, 2015, at 10:52 PM, David Winsemius <dwinsem...@comcast.net> wrote: > >> >> On Feb 12, 2015, at 5:50 PM, Steve Bellan wrote: >> >>> Hi all, I'm fitting a coxph gamma frailty model to simulated survival data >>> and running into situations where I have 0 events in one covariate class >>> and the model won't converge. I'd still like a p-value in those cases as >>> this is part of a power analysis. With enough person-time observed 20 >>> events in one group and 0 in another is likely significant, but I want a >>> p-value to be sure. Firth's correction in ‘coxphf’ seems appropriate but >>> coxphf doesn't seem to deal with random effects. Any suggestions would be >>> much appreciated! >>> >> >> I would have expected power analyses in mixed model situations to be >> conducted with bootstrap methods. In that setting you could just collect the >> zero event cases in one category and use then as part of the denominator. >> >> -- >> >> David Winsemius >> Alameda, CA, USA >> > David Winsemius Alameda, CA, USA _______________________________________________ R-sig-Epi@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-epi
