Re: Single-Factor-Cox-Regression
- From: Richard Ulrich <Rich.Ulrich@xxxxxxxxxxx>
- Date: Sat, 24 Feb 2007 18:33:12 -0500
On 23 Feb 2007 12:08:09 -0800, patrick.ringsmoeller@xxxxxxxx wrote:
Hello everybody,
I would like to test the influence of a clinical parameter upon
patient survival by Cox-Regression. But I am not sure, whether this is
allowed (using only a single factor (covariate) within Cox-Regression)
or if Cox-Regression always needs a certain amount of different
covariates to be correct in a statistical sense. Moreover, I wondered
if there exists a rule of thumb how many covariates can be included in
a Cox-Regression at maximum for a given number of patients under
consideration.
The Cox proportionate hazard regression can is surely
safer with one variable than with several.
I have not seen mention of limits for covariates.
I have run a number of them, with fairly small N, and at some
point, it can fail to converge, or give bad numbers. Run some
with your own data -- Then try it with partial samples -- Does it
seem to work?
Like the logistic regression, which it is sort-of an extension
of, it is probably best used as a large sample procedure.
That is, there is a dichotomous criterion at each of the
multiple periods, and there is a further assumption that the
rates for two groups have the same Odds Ratio at each
of the several periods.
One conservative guideline that has been mentioned for
Logistic regression is to require at least 20 more cases in the
smaller category, for each additional covariate. (I think that
requiring "20" is being conservative; you are almost assured of
having no problems with an N that large, but you might do
pretty well with half that many... or, you might not.)
Logistic regression has one outcome; Cox regression has
outcomes at many periods. So it will need a bigger N.
I *think* that the Cox regressions are implemented around
discrete periods, so that you need cases in both groups at
every period. Thus, your N for Cox regression is probably
a multiple of what you need for Logistic regression, depending
on the number of periods.
If anyone knows something more definite about that, I will
be happy to hear it.
--
Rich Ulrich, wpilib@xxxxxxxx
http://www.pitt.edu/~wpilib/index.html
.
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