basehaz() in package 'Survival' and warnings() with coxph
On Aug 9, 2012, at 5:53 PM, hazbro wrote:
My sessionInfo is as follows: R version 2.15.1 (2012-06-22) Platform: x86_64-unknown-linux-gnu (64-bit)
snip
It will be difficult to reproduce an example here as the data set I am using in very large. I can give you an example: fit3.1<- coxph(formula = y ~ sex + ns(ageyrs, df = 2) + AdmissionSource + + X1 + X2 + X3 + X5 + X6 + X7 + X11 + X12 + X13 + X14 + X15 + + X16 + X17 + X18 + X19 + X20 + X22 + X24 + X25 + X26 + X27 + + X28 + X29 + X32 + X33 + X35 + X38 + X39 + X40 + X41 + X42 + + X43 + X44 + X47 + X49 + X53 + X54 + X55 + X58 + X59 + X62 + + X68 + X69 + X78 + X80 + X81 + X84 + X85 + X86 + X93 + X95 + + X98 + X100 + X101 + X102 + X105 + X107 + X108 + X109 + X110 + + X112 + X113 + X114 + X115 + X116 + X117 + X121 + X122 + X125 + + X127 + X128 + X129 + X131 + X132 + X133 + X134 + X138 + X140 + + X143 + X145 + X146 + X148 + X150 + X151 + X153 + X157 + X158 + + X159 + X164 + X197 + X200 + X202 + X203 + X204 + X205 + X211 + + X214 + X217 + X224 + X228 + X233 + X237 + X244 + X249 + X254 + + X258 + X259 + X260 + CharlsonIndex + ethnic + day + season + + ln, data = dat2)
haz<-basehaz(fit3.1) # gives 507 unique haz$time, time points fit2<-coxph(y~ns(ageyrs,df=2)+day+ln+sex+AdmissionSource+season +CharlsonIndex,data=dat1) haz<-basehaz(fit2) # gives 611 unique haz$time, time points
Regardless of the discrepancy it appears you have over 1-200 variables with only 5-600 events.
I get the following warnings() with fit3.1: Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Loglik converged before variable ; beta may be infinite. Also the coefficients of the variables that the error occurs for are very high.
That suggests that the warning should be heeded because you probably have numerical stability problems, possibly highly collinear variables or complete separation on various strata.
The Wald test suggests dropping these terms where as the LRT suggests keeping them. What should I do in terms of model selection?
I worry that you have already committed many modeling sins. If you started out with 260 variables and dropped a bunch of them with step down procedur, then you are currently underestimating the number of degrees of freedom that you should be using. My guess is that if you used the proper degrees of freedom that the LRT would not support keeping them. You have too few data points to support that many variables. As Bert Gunter often recommends... get thee to a statistician.
-- View this message in context: http://r.789695.n4.nabble.com/basehaz-in-package-Survival-and-warnings-with-coxph-tp4639687p4639838.html Sent from the R help mailing list archive at Nabble.com.
David Winsemius, MD Alameda, CA, USA