Thanks Chuck. I agree that�s a good idea but I�m not too familiar with the Bayesian coxph survival packages and on first scan of
http://cran.r-project.org/web/views/Survival.html I didn't see any that handle frailties except for survBayes (http://ftp.auckland.ac.nz/software/CRAN/src/contrib/Descriptions/survBayes.html) which has been removed from CRAN for undocumented reasons. I couldn�t get it to work either. Recommendations on packages would be much appreciated. Steve On Feb 15, 2015, at 11:34 AM, Rose, Charles E. (CDC/OID/NCHHSTP) <c...@cdc.gov> wrote: > I may be missing part of the conversation but it seems like Bayesian is a > viable alternative, chuck > > > -----Original Message----- > From: R-sig-Epi [mailto:r-sig-epi-boun...@r-project.org] On Behalf Of Steve > Bellan > Sent: Sunday, February 15, 2015 12:29 PM > To: David Winsemius > Cc: r-sig-epi@r-project.org > Subject: Re: [R-sig-Epi] coxphf with frailty, Firth's correction > > I don't quite see how bootstrapping would help. > > Say I have 20 clusters, with 10 receiving a treatment and 10 control. Say I > have 0 events in the treatment cluster and 22 events distributed amongst a > handful of the control clusters. If I bootstrap, resampling at the cluster > level with replacement, then no matter what I will always have 0 events in > the bootstrapped treatment clusters. One can't resample 0 events to get more > than 0 events. And coxph models are divergent when one treatment class has 0 > events. Furthermore the effect size estimate for a relative hazard between 0 > events and >0 events will always be -Inf (on a log-hazard scale). So I won't > be able to estimate variation in the effect size from a bootstrap. Am I > missing something? > > I could see how a reshuffling algorithm could work to get a P value-i.e. > randomly relabeling 10 clusters to be treatment and 10 to be control, then > estimating the effect size from a coxph frailty model, and using this to > create a null distribution of effect sizes. But I still wouldn't be able to > get a confidence interval. This seems like the best approach unless Firth's > correction for monotonic likelihoods could be applied here. > > On Feb 14, 2015, at 12:21 AM, David Winsemius <dwinsem...@comcast.net> wrote: > >> >> 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 [[alternative HTML version deleted]]
_______________________________________________ R-sig-Epi@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-epi
