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# Re: Subject: Re: st: Survival function of regression Cox model postestimation

 From "Airey, David C" To "statalist@hsphsun2.harvard.edu" Subject Re: Subject: Re: st: Survival function of regression Cox model postestimation Date Thu, 14 Jul 2011 10:27:23 -0500

```.

I've not much experience with survival models other than log-rank tests, but am reading more about them now.

What's the interpretation of a continuous covariate if it doesn't include 0? A transformation can be done on an X covariate, e.g., X-#, to get the relative risk to when X=#. But if you don't do this transformation, and your data range doesn't naturally include 0, what happens to the interpretation? What do you do with a continuous covariate that varies from (-) to (+) values and you don't want the given 0 to be used?

-Dave

--- On Tue, Jul 12, 2011 at 6:40 PM, Mario Petretta wrote me privately:
> Anyway, there is a rule for applying a non-linear transformation to a
> continuous covariate in order to obtain a survival function of a Cox model
> with that covariate obtained at its mean value not different from
> baseline survival function of the Cox model without covariates?

It is better to respond to the Statalist and not privately because if
you find my answer unclear, chances are that other people following
this thread may also find it unclear.

No, Cox regression has a baseline hazard function not a baseline
survival function, and the baseline hazard function is the hazards at
different points in time when all explanatory/independent/x-variables
are 0 not fixed at the mean.

Hope this helps,
Maarten
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