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Cited by 349

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models

Open access
Pencina, Michael J., D'Agostino, Ralph B., Demler, Olga V.·2012·Statistics in Medicine
DOI 10.1002/sim.4348
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Abstract

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.

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Metadata

Authors
Pencina, M. J., D'Agostino, R. B., & Demler, O. V.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2011-12-07
Volume
31
Issue
2
Pages
101-113
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.4348
OpenAlex
W1990479835
Cited by
349
References
37
FWCI
16.41
Type
journal article
Language
en
Open access
bronze

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