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Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve

Combining Predictors for Classification Using the Area under the Receiver Operating... Summary No single biomarker for cancer is considered adequately sensitive and specific for cancer screening. It is expected that the results of multiple markers will need to be combined in order to yield adequately accurate classification. Typically, the objective function that is optimized for combining markers is the likelihood function. In this article, we consider an alternative objective function—the area under the empirical receiver operating characteristic curve (AUC). We note that it yields consistent estimates of parameters in a generalized linear model for the risk score but does not require specifying the link function. Like logistic regression, it yields consistent estimation with case–control or cohort data. Simulation studies suggest that AUC‐based classification scores have performance comparable with logistic likelihood‐based scores when the logistic regression model holds. Analysis of data from a proteomics biomarker study shows that performance can be far superior to logistic regression derived scores when the logistic regression model does not hold. Model fitting by maximizing the AUC rather than the likelihood should be considered when the goal is to derive a marker combination score for classification or prediction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Oxford University Press

Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve

Pepe, Margaret Sullivan; Cai, Tianxi; Longton, Gary
Biometrics , Volume 62 (1) – Mar 1, 2006
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References (24)

Publisher
Oxford University Press
Copyright
Copyright © 2006 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0006-341X
eISSN
1541-0420
DOI
10.1111/j.1541-0420.2005.00420.x
pmid
16542249
Publisher site
See Article on Publisher Site

Abstract

Summary No single biomarker for cancer is considered adequately sensitive and specific for cancer screening. It is expected that the results of multiple markers will need to be combined in order to yield adequately accurate classification. Typically, the objective function that is optimized for combining markers is the likelihood function. In this article, we consider an alternative objective function—the area under the empirical receiver operating characteristic curve (AUC). We note that it yields consistent estimates of parameters in a generalized linear model for the risk score but does not require specifying the link function. Like logistic regression, it yields consistent estimation with case–control or cohort data. Simulation studies suggest that AUC‐based classification scores have performance comparable with logistic likelihood‐based scores when the logistic regression model holds. Analysis of data from a proteomics biomarker study shows that performance can be far superior to logistic regression derived scores when the logistic regression model does not hold. Model fitting by maximizing the AUC rather than the likelihood should be considered when the goal is to derive a marker combination score for classification or prediction.

Journal

Biometrics – Oxford University Press

Published: Mar 1, 2006

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