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Re: st: Transformed values in logistic regression
Thank you to all that replied. Although there appears
to be no consensus about whether the referee is
correct in his assessment, there is consensus that the
analysis will need to be modified (perhaps needlessly)
only to satisfy the reviewer’s comment.
Thank you again,
--- Tim Wade <firstname.lastname@example.org> wrote:
> --- Ricardo Ovaldia <email@example.com> wrote:
> > Dear all,
> > Please pardon this non-Stata question, but I have
> > not
> > found a satisfactory resolution.
> > We recently submitted a manuscript for publication
> > and
> > received a favorable reply, except that one of the
> > reviewers criticized a transformation that we
> > performed on a continuous variable prior to
> > a
> > logistic regression model.
> > Specifically we were interested in modeling
> > case-control status as a function of several
> > covariates including serum creatinine which in our
> > data ranges from 0.11 to 1.98.
> > Because of skewness and to make the odds ratio
> > independent of the units measurement, we decided
> > log-transform the creatinine values before
> > them into our logistic model. However the reviewer
> > wrote “Using a log-transform for creatine is
> > because a 1-unit increase in ln(x) is equivalent
> > increasing x by a factor of 2.718 which is in the
> > realm of impossibility”
> Ricardo, I think the reviewer might be partially
> correct because a 1 ln increase is beyond the range
> the data, the coefficient associated with such an
> increase in creatinine would be large and possibly
> misleading. You could simply use "lincom
> 0.1*creatinine" to rescale the coefficient/odds
> to a more meaningful unit increase.
> > Is he correct? Isn’t the coefficient estimated
> > that the predicted values are within the range of
> > the
> > data and this only a problem if you attempt to
> > extrapolate beyond the data range? What I am
> > missing?
> > Thank you very much,
> > Ricardo.
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Ricardo Ovaldia, MS
Oklahoma City, OK
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