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


From   Diego Bellavia <messadua@yahoo.it>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: Survival analysis: finding best cut-off values
Date   Wed, 7 Mar 2007 17:13:38 +0000 (GMT)

This article helped a lot, 

Thank you so much !

Diego

----- Messaggio originale -----
Da: Richard Goldstein <richgold@ix.netcom.com>
A: statalist@hsphsun2.harvard.edu
Inviato: Marted́ 6 marzo 2007, 15:46:59
Oggetto: Re: st: RE: Survival analysis: finding best cut-off values


I'm not sure exactly what you are looking for or want;
however, if the formulation of something like the
Framingham heart risk score is at all related, you
might want to look at: Sullivan, LM, et al. (2004),
"Presentation of multivariate data for clinical use:
the Framingham Study risk score functions," _Statistics
in Medicine_, 23: 1631-1660

Rich

Diego Bellavia wrote:

> 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 ? 
> 
> Diego
> 
> ----- Messaggio originale -----
> Da: Nick Cox <n.j.cox@durham.ac.uk>
> A: statalist@hsphsun2.harvard.edu
> Inviato: Marted́ 6 marzo 2007, 15:29:04
> Oggetto: st: RE: Survival analysis: finding best cut-off values
> 
> 
> The practice of dividing good continuous
> variables into categories is retrograde. 
> See Frank Harrell's book on "Regression modeling
> strategies" from Springer in 2001. 
> 
> Nick 
> n.j.cox@durham.ac.uk 
> 
> 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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