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RE: st: RE: Survival analysis: finding best cut-off values


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: Survival analysis: finding best cut-off values
Date   Tue, 6 Mar 2007 21:40:07 -0000

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 
n.j.cox@durham.ac.uk 

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 <n.j.cox@durham.ac.uk>
 
> 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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