Asymptotic Distribution Theory and Efficiency Results for Case-Cohort Studies

Abstract

A case-cohort design was recently proposed [Prentice (1986)] as a means of reducing cost in large epidemiologic cohort studies. A "pseudolikelihood" procedure was described for relative risk regression parameter estimation. This procedure involves covariate data only on subjects who develop disease and on a random subset of the entire cohort. In contrast, the usual partial likelihood estimation procedure requires covariate histories on the entire cohort. Accordingly, with large cohorts and infrequent disease occurrence. Asymptotic theory for such pseudolikelihood estimators, ing cumulative failure rate estimators, efficiency are presented here. Certain asymptotic expressions relative to full-cohort estimators are developed and tabulated in situations of relevance to the design of large-scale disease prevention trials. The theoretical developments make use of martingale convergence results and finite population convergence results