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st: RE: net proportion of subjects with improved prediction


From   "Daniel Waxman" <dan@amplecat.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: net proportion of subjects with improved prediction
Date   Sat, 29 Sep 2007 12:40:56 -0400

Ben,

Thank you very much for thinking about this.

I have been trying to figure out how my index relates to the between-model
difference in the area under the ROC curves.††† The difference in AUCs are
notorious for sometimes being very small even when otherwise seemingly
important predictors are added to a model.† †I know that one interpretation
of the AUC is as †the probability that a subject with the outcome of
interest will be ranked higher than a subject without it, but Iím having a
hard time figuring out whether there is a direct transformation between AUC
differences and the index that I propose, which would offer a quantitative
measure of the number (or proportion) of patients correctly reassigned with
the new model.

Dan

***********

From: Chapman, Ben P [mailto:Ben_Chapman@URMC.Rochester.edu] 
Sent: Saturday, September 29, 2007 9:39 AM
To: dan@amplecat.com
Subject: net proportion of subjects with improved prediction

Hi Dan--not sure if this will be helpful or not; I've run into a similar
issue of trying to demonstrate that added variable(s) enhance overall
predictive accuracy in logit models.
†
This analysis at the UCLA web site shows a very interesting method
(apparently published in the stata journal) for demonstrating that added
predictor(s) enhance AUC--if you consider improvements in sensitivity v.
1-specificity as a net or overall measure of improved prediction--not sure
if it is exactly what you're looking for but thought I'd point it out...
†
http://www.ats.ucla.edu/stat/stata/faq/roc.htm
†
Best,
Ben Chapman
†
Benjamin Chapman, PhD
NRSA Postdoctoral Fellow
MPH Candidate
University of Rochester School of Medicine and Dentistry
Department of Psychiatry
Laboratory of Personality and Development
Rochester Center for Mind Body Research
†

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