Circulation. 2007;115:928-935
doi: 10.1161/CIRCULATIONAHA.106.672402
doi: 10.1161/CIRCULATIONAHA.106.672402
(Circulation. 2007;115:928-935.)
© 2007 American Heart Association, Inc.
Special Report |
Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction
From the Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass, and the Department of Epidemiology, Harvard School of Public Health, Boston, Mass.
Correspondence to Dr Nancy R. Cook, Division of Preventive Medicine, Brigham and Women’s Hospital, 900 Commonwealth Ave East, Boston, MA 02215. E-mail ncook{at}rics.bwh.harvard.edu
The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the cstatistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
Key Words: cardiovascular diseases • epidemiology • follow-up studies • prevention • risk factors • statistics • risk
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M. Berkwits and E. Guallar Risk Factors, Risk Prediction, and the Apolipoprotein B-Apolipoprotein A-I Ratio Ann Intern Med, May 1, 2007; 146(9): 677 - 679. [Full Text] [PDF]
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D. A. Morrow and J. A. de Lemos Benchmarks for the Assessment of Novel Cardiovascular Biomarkers Circulation, February 27, 2007; 115(8): 949 - 952. [Full Text] [PDF]
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