Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models
Article first published online: 7 DEC 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 2, pages 101–113, 30 January 2012
How to Cite
Pencina, M. J., D'Agostino, R. B. and Demler, O. V. (2012), Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Statist. Med., 31: 101–113. doi: 10.1002/sim.4348
- Issue published online: 28 DEC 2011
- Article first published online: 7 DEC 2011
- Manuscript Accepted: 30 JUN 2011
- Manuscript Received: 26 JAN 2011
- National Heart, Lung, and Blood Institute's Framingham Heart Study. Grant Number: N01-HC-25195
- NIH/ARRA Risk Prediction of Atrial Fibrillation. Grant Number: RC1HL101056
- model performance;
- risk prediction;
Net reclassification and integrated discrimination improvements have been proposed as alternatives to the increase in the area under the curve for evaluating improvement in the performance of risk assessment algorithms introduced by the addition of new phenotypic or genetic markers. In this paper, we demonstrate that in the setting of linear discriminant analysis, under the assumptions of multivariate normality, all three measures can be presented as functions of the squared Mahalanobis distance. This relationship affords an interpretation of the magnitude of these measures in the familiar language of effect size for uncorrelated variables. Furthermore, it allows us to conclude that net reclassification improvement can be viewed as a universal measure of effect size. Our theoretical developments are illustrated with an example based on the Framingham Heart Study risk assessment model for high-risk men in primary prevention of cardiovascular disease. Copyright © 2011 John Wiley & Sons, Ltd.