Thomas Lumley-2 wrote: > > [...] > > The warning and error messages are correct here. Look at the point > estimate. It's a log hazard ratio of about 20 in one case and about > -20 in the other case. The true partial maximum likelihood estimator > is infinite. The estimated standard errors are meaningless, since the > partial likelihood isn't close to quadratic at the maximum. > > [...] > I see. It explains the results for these testing data sets.
But, with my real data set I get these results : With SAS : estimate FERM : 1.47654 se : 0.03117 Pr > Khi 2 : <.0001 hazard ratio : 4.378 convergence status : "Convergence criterion (GCONV=1E-8) satisfied." This time, the hazard ratio is not big. The maximum of the partial likelihood seems to be reached. The program takes about 45 seconds to finish computation. My sample contains 6588 observations with a lot of ties (discrete time values). With R : I don't get any result. The program freezes and does not respond. I waited for about 1 hour without a result. So can I conclude in this case that the problem with the "coxph" function is due to computation power rather than another algorithmic problem ? -- View this message in context: http://r.789695.n4.nabble.com/comparing-SAS-and-R-survival-analysis-with-time-dependent-covariates-tp874438p3680340.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
