Thanks to all that replied. Just to be clear the
reviewer states "transformations
(logarithmic or others as appropriate) are needed to
normalize the distribution of skewed variables". This
is clearly NOT the reason for wanting to transform
independent variables in the Cox model.
Thank you,
Ricardo.
--- Ron�n Conroy <[email protected]> wrote:
> On 3 Feabh 2006, at 20:09, Ricardo Ovaldia wrote:
>
> > A reviewer of a manuscript that we recently
> submitted
> > to a top medical journal stated that the
> "independent
> > variable, known to be positively skewed , be
> > log-transformed prior to inclusion in a Cox
> > proportional hazards model". As far as I know
> there is
> > no normality assumption for the Cox model,
> > additionally transforming this complicates the
> > interpretation of the reported hazard ratios. Am I
> > missing something or is the reviewer wrong? Other
> than
> > for interpretation, is there another reason to
> > transform an independent variable in the Cox PH
> model?
>
> The distribution of the variable will affect the
> ease of
> interpretation of the hazard ratios. Given the
> distribution, a hazard
> ratio associated with an n-fold increase in the
> predictor variable
> might make more sense than a 1-unit increase.
>
> My own hunch is that a variable with a funny
> distribution might
> better be re-expressed in, say, deciles or
> quintiles. This makes the
> hazard ratio easily explainable. You can also do
> this for variables
> on different scales, making your hazard ratios
> comparable across
> model terms. And quantiling will deal with zero
> values, otherwise a
> tough problem on a log scale.
>
> Ron�n Conroy
> [email protected]
>
>
>
>
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>
Ricardo Ovaldia, MS
Statistician
Oklahoma City, OK
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