On Wed, 2011-05-11 at 16:11 -0700, Shi, Tao wrote: > Hi all, > > I found that the two different versions of "survival" packages, namely 2.36-5 > vs. 2.36-8 or later, give different results for coxph function. Please see > below and the data is attached. The second one was done on Linux, but > Windows > gave the same results. Could you please let me know which one I should trust? > > Thanks,
In your case, neither. Your data set has 22 events and 17 predictors; the rule of thumb for a reliable Cox model is 10-20 events per predictor which implies no more than 2 for your data set. As a result, the coefficients of your model have very wide confidence intervals, the coef for Male for instance has se of 3.26, meaning the CI goes from 1/26 to 26 times the estimate; i.e., there is no biological meaning to the estimate. Nevertheless, why did coxph give a different answer? The later version 2.36-9 failed to converge (20 iterations) with a final log-likelihood of -19.94, the earlier code converges in 10 iterations to -19.91. In version 2.36-6 an extra check was put into the maximizer for coxph in response to an exceptional data set which caused the routine to fail due to overflow of the exp function; the Newton-Raphson iteration algorithm had made a terrible guess in it's iteration path, which can happen with all NR based search methods. I put a limit on the size the linear predictor in the Cox model of 21. The basic argument is that exp(linear-predictor) = relative risk for a subject, and that there is not much biological meaning for risks to be less than exp(-21) ~ 1/(population of the earh). There is more to the reasoning, interested parties should look at the comments in src/coxsafe.c, a 5 line routine with 25 lines of discussion. I will happily accept input the "best" value for the constant. I never expected to see a data set with both convergence of the LL and linear predictors larger than +-15. Looking at the fit (older code) > round(fit2$linear.predictor, 2) [1] 2.26 0.89 4.96 -19.09 -12.10 1.39 2.82 3.10 [9] 18.57 -25.25 22.94 8.75 5.52 -27.64 14.88 -23.41 [17] 13.70 -28.45 -1.84 10.04 12.62 2.54 6.33 -8.76 [25] 9.68 4.39 2.92 3.51 6.02 -17.24 5.97 This says that, if the model is to be believed, you have several near immortals in the data set. (Everyone else on earth will perish first). Terry Therneau ______________________________________________ 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.
