Biometrika Advance Access originally published online on October 12, 2009
Biometrika 2009 96(4):991-997; doi:10.1093/biomet/asp040
Miscellanea |
Semiparametric methods for evaluating risk prediction markers in case-control studies
Fred Hutchinson Cancer Research Center, Public Health Sciences, 1100 Fairview Avenue N., Seattle, Washington 98109-1024, U.S.A. yhuang{at}fhcrc.org mspepe{at}u.washington.edu
Received for publication 1 March 2008. Revision received 1 January 2009.
The performance of a well-calibrated risk model for a binary disease outcome can be characterized by the population distribution of risk and displayed with the predictiveness curve. Better performance is characterized by a wider distribution of risk, since this corresponds to better risk stratification in the sense that more subjects are identified at low and high risk for the disease outcome. Although methods have been developed to estimate predictiveness curves from cohort studies, most studies to evaluate novel risk prediction markers employ case-control designs. Here we develop semiparametric methods that accommodate case-control data. The semiparametric methods are flexible, and naturally generalize methods previously developed for cohort data. Applications to prostate cancer risk prediction markers illustrate the methods.
Key Words: Biased sampling • Biomarker • Case-control • Predictiveness curve • Risk prediction • Semiparametric method
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