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Joint Modeling, Covariate Adjustment, and Interaction: Contrasting Notions in Risk Prediction Models and Risk Prediction Performance

Kerr, Kathleen F.; Pepe, Margaret S.

doi: 10.1097/EDE.0b013e31823035fb

Epidemiologic methods are well established for investigating the association of a predictor of interest and disease status in the presence of covariates also associated with disease. There is less consensus on how to handle covariates when the goal is to evaluate the increment in prediction performance gained by a new marker when a set of predictors already exists. We distinguish between adjusting for covariates and joint modeling of disease risk in this context. We show that adjustment and joint modeling are distinct concepts, and we describe the specific conditions where they are the same. We also discuss the concept of interaction among variables and describe a notion of interaction that is relevant to prediction performance. We conclude with a discussion of the most appropriate methods for evaluating new biomarkers in the presence of existing predictors.

From the Department of Biostatistics and Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA.

Submitted 4 January 2011; accepted 15 July 2011.

Supported by NIH grants GM 54438 and CA 86368 (M.S.P.) and University of Washington sabbatical funding (K.F.K.).

Editors' note: A commentary on this article appears on page xxx.

Correspondence: Kathleen F. Kerr, Department of Biostatistics, Box 357232 University of Washington, Seattle, WA 98195. E-mail:

© 2011 Lippincott Williams & Wilkins, Inc.