All right !
Thank you anyway,
At least now I know what I should not do.
Best
Diego
----- Messaggio originale -----
Da: Nick Cox <[email protected]>
A: [email protected]
Inviato: Marted� 6 marzo 2007, 15:40:07
Oggetto: RE: st: RE: Survival analysis: finding best cut-off values
I don't know what that means. In any case, many others
on this list know much more about using Cox models than I do.
Nick
[email protected]
Diego Bellavia
> mmhh, Ok.
>
> I will not do that in the future, but then, what is the most
> efficient way to find
> cut-off values for predictors ?
Nick Cox <[email protected]>
> The practice of dividing good continuous
> variables into categories is retrograde.
> See Frank Harrell's book on "Regression modeling
> strategies" from Springer in 2001.
Diego Bellavia
> > I am performing a survival analysis on a dataset with many
> > variables. Multivariate cox proportional-hazard models
> > defined the best predictors (around 7 out of 270 variables).
> > I would like to give the readers some cut-off values
> > they can use in the clinical practice, so I divided the most
> > significant predictors in tertiles, create the dummy variables
> > and run Cox models for each variable (groups of dummy vars).
> > Doing so, I obtain significant/unsignificant tertiles and
> > Kaplan-Meyer graphs
> > stratified by tertiles. Thsi way works pretty well. But what
> > if I would like to find only one cut-off per variable ?
> > I thought to use ROC curves to define the best diagnostic
> > cut-offs and see if they are good also for prognosis, but
> > unfortunately not all the best
> > predictors are so good also to discriminate groups of patients.
> > In conclusion my question is: there is a way to obtain the
> > best prognostic cut-off value using Cox models ?
>
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