> Loglik converged before variable 1,2 ; beta may be infinite.
> I have two (related) questions:
> 1. Can I in general ignore this kind of warning and just pay attention to
> predictors which produce significant p values?
> 2. In the specific case of stepAIC, can I ignore this warning and trust
> that stepAIC will remove predictors that aren't useful contributors
> to the model.
Usually you can ignore the message, it is mostly for your information. The
key things to note are
a. When one of the coefficients goes to infinity in a Cox model, the Wald
test of significance beta/se(beta) breaks down, and is no longer reliable.
The LR test however is still valid. Hence routines like stepAIC are ok.
So are predicted values, residuals, etc etc. In fact it is pretty much only
the
Wald test that needs to be ignored: it is based on a Taylor series that simply
doesn't work that far from zero. Oops -- confidence intervals based on the se
are also useless.
b. The actual value of beta that is reported depends on the convergence
criteria for the routine. So this is one case where different Cox model
functions can give results that look different. I work in medical research,
and
view these differences as unimportant: if I were to tell you that your relative
risk of death was exp(11) = 59,774 fold greater than your compatriots, would
the
message be substantially changed for beta of 10 or 12?
There is a statistical literature under the heading of "monotone likelihood
ratio" that worries about these coefficients and tries to fix them. Much ado
about nothing, IMHO.
c. For a large beta and a very skewed covariate the message can sometimes be
wrong. Beta is finite, just unstable. I might still prefer the LR to the Wald
in this case. Spline fits based on the truncated power basis (which Frank
Harrell uses) are one way to generate such spurious messages.
Frank has argued with me that these messages may be shedding more confusion
than illumination. He has a point.
Terry Therneau
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