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 <[email protected]>
A: [email protected]
Inviato: Marted� 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
[email protected]
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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