> I also see this sort of thing done sometimes to make results more easily
> interpretable (and applicable?) for doctors and other clinicians.  Some of
> them struggle with interpretation of coefficients for continuous
> variables, and have a clear preference for categorical predictor
> variables.
> 


This is a good reason, for instance, dietary advice often involves
dramatic reduction in things like saturated fat, so you would compare
90th percentile to 10th percentile, (say in a logistic regression for
risk of heart disease) not caring about the Beta, of the full spectrum
of saturated fat.
An Odds Ratio of 1.5 looks better than an odds ratio of 1.00001
.
.
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