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

From   Richard Goldstein <>
Subject   Re: st: RE: Survival analysis: finding best cut-off values
Date   Tue, 06 Mar 2007 16:46:59 -0500

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


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 ?

----- Messaggio originale -----
Da: Nick Cox <>
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.
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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