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st: Survival analysis: finding best cut-off values


From   Diego Bellavia <messadua@yahoo.it>
To   STATAlist <statalist@hsphsun2.harvard.edu>
Subject   st: Survival analysis: finding best cut-off values
Date   Tue, 6 Mar 2007 21:24:58 +0000 (GMT)

Dear Statalisters, 

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 ? 

Thank you in advance for any help/insight 

Diego


	

	
		
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