Semiparametric methods for evaluating risk prediction markers in case-control studies

• 成果类型: Article
• 署名作者: Huang, Ying; Pepe, Margaret Sullivan
• 署名单位: Fred Hutchinson Cancer Center
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asp040
• 发表日期: 2009
• 页码: 991997
• 关键词: maximum-likelihood-estimation; large-sample theory; empirical distributions; logistic-regression; models; EFFICIENCY; phases

摘要

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.