ASYMPTOTIC THEORY FOR NESTED CASE-CONTROL SAMPLING IN THE COX REGRESSION-MODEL

• 成果类型: Article
• 署名作者: GOLDSTEIN, L; LANGHOLZ, B
• 署名单位: University of Southern California
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176348895
• 发表日期: 1992
• 页码: 1903-1928
• 关键词: case-cohort; counting-processes; cancer; DESIGN; RISK

摘要

By providing a probabilistic model for nested case-control sampling in epidemiologic cohort studies, consistency and asymptotic normality of the maximum partial likelihood estimator of regression parameters in a Cox proportional hazards model can be derived using process and martingale theory as in Andersen and Gill. A general expression for the asymptotic variance is given and used to calculate asymptotic relative efficiencies relative to the full cohort variance in some important special cases.