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Re: st: AIC and BIC to compare parametric and non-parametric survival models


From   Ronan Conroy <rconroy@rcsi.ie>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: AIC and BIC to compare parametric and non-parametric survival models
Date   Tue, 19 May 2009 09:22:01 +0100

On 18 Beal 2009, at 18:27, Tom Trikalinos wrote:

To compare non-parametric and parametric survival
analysis models, can I use the AIC and BIC?
Specifically, I fit Cox PH models and exponential and
weibull parametric regressions. It was pointed out to
me that AIC & BIC-based comparisons may not be valid
(because Cox uses partial likelihood).

PS. I am performing survival analyses to inform a decision
analysis. For this reason I strongly prefer to fit
parametric models - will make life easier and restore the
smile on me face.


Comparing models for decision making involves assessing the validity and utility of the decisions they make. This is not a matter of model fit but of defining the characteristics of a desirable model.

For example, if you are developing a model to assess acute chest pain, then the critical model errors are false negatives - errors that will result in someone with an evolving heart attack being sent home. On the other hand, in many screening situations the problem is false positives, who will place a burden on diagnostic services.

Ideally, you need to assess your model in a fresh sample. While you can hold back some of your estimation sample for validation, this poses the problem that factors such as sampling bias, measurement bias etc are common to your estimation and validation samples.

I would go for the performance indicators: sensitivity, specificity, positive and negative predictive value. Decision makers understand them.

I have seen numerous statistically significant disease predictors that made no difference at all the clinical decision making. Indeed, useful predictors are far rarer than significant ones.



Ronan Conroy
=================================

rconroy@rcsi.ie
Royal College of Surgeons in Ireland
Epidemiology Department,
Beaux Lane House, Dublin 2, Ireland
+353 (0)1 402 2431
+353 (0)87 799 97 95
+353 (0)1 402 2764 (Fax - remember them?)
http://rcsi.academia.edu/RonanConroy

P    Before printing, think about the environment




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