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American Journal of Epidemiology Advance Access originally published online on November 2, 2007
American Journal of Epidemiology 2008 167(3):362-368; doi:10.1093/aje/kwm305
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American Journal of Epidemiology © The Author 2007. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

PRACTICE OF EPIDEMIOLOGY

Integrating the Predictiveness of a Marker with Its Performance as a Classifier

Margaret S. Pepe1,2, Ziding Feng1, Ying Huang2, Gary Longton1, Ross Prentice1, Ian M. Thompson3 and Yingye Zheng1

1 Fred Hutchinson Cancer Research Center, Seattle, WA
2 University of Washington, Seattle, WA
3 University of Texas Health Sciences Center, San Antonio, TX

Correspondence to Dr. Margaret Sullivan Pepe, Biostatistics and Biomathematics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B500, Seattle, WA 98109 (e-mail: mspepe{at}u.washington.edu).

Received for publication March 15, 2007. Accepted for publication September 18, 2007.

There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic regression. A marker is considered useful if it has a strong effect on risk. The second evaluates classification performance by use of measures such as sensitivity, specificity, predictive values, and receiver operating characteristic curves. There is controversy about which approach is more appropriate. Moreover, the two approaches can give contradictory results on the same data. The authors present a new graphic, the predictiveness curve, which complements the risk modeling approach. It assesses the usefulness of a risk model when applied to the population. Although the predictiveness curve relates to classification performance measures, it also displays essential information about risk that is not displayed by the receiver operating characteristic curve. The authors propose that the predictiveness and classification performance of a marker, displayed together in an integrated plot, provide a comprehensive and cohesive assessment of a risk marker or model. The methods are demonstrated with data on prostate-specific antigen and risk factors from the Prostate Cancer Prevention Trial, 1993–2003.

biological markers; classification analysis; diagnostic tests, routine; epidemiologic methods; predictive value of tests; prostate-specific antigen; risk assessment; risk model

Abbreviations: CI, confidence interval; FPF, false positive fraction; PSA, prostate-specific antigen; ROC, receiver operating characteristic; TPF, true positive fraction


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