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Biostatistics 5:113-127 (2004)
© 2004 Oxford University Press

Quantifying and comparing the predictive accuracy of continuous prognostic factors for binary outcomes

Chaya S. Moskowitz* and Margaret S. Pepe

Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 307 E 63rd Street, 3rd Floor, New York, NY 10021, USA moskowc1{at}mskcc.org
Departments of Biostatistics, University of Washington and Fred Hutchinson Cancer Research Center, Box 357232, Seattle, WA 98195-7232, USA

*To whom correspondence should be addressed.

The positive and negative predictive values are standard ways of quantifying predictive accuracy when both the outcome and the prognostic factor are binary. Methods for comparing the predictive values of two or more binary factors have been discussed previously (Leisenring et al., 2000, Biometrics 56, 345–351). We propose extending the standard definitions of the predictive values to accommodate prognostic factors that are measured on a continuous scale and suggest a corresponding graphical method to summarize predictive accuracy. Drawing on the work of Leisenring et al. we make use of a marginal regression framework and discuss methods for estimating these predictive value functions and their differences within this framework. The methods presented in this paper have the potential to be useful in a number of areas including the design of clinical trials and health policy analysis.

Keywords: Classification; Generalized estimating equations; Positive predictive values; Prediction; ROC curves


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