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.
--
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--
David Winsemius, MD
Alameda, CA, USA
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