Asymptotic Theory for Nested Case-Control Sampling in the Cox Regression Model

Abstract

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.