Semiparametric empirical likelihood estimation for two-stage outcome-dependent sampling under the frame of generalized linear models

Abstract

Epidemiologic studies use outcome-dependent sampling (ODS) schemes where, in addition to a simple random sample, there are also a number of supplement samples that are collected based on outcome variable. ODS scheme is a cost-effective way to improve study efficiency. We develop a maximum semiparametric empirical likelihood estimation (MSELE) for data from a two-stage ODS scheme under the assumption that given covariate, the outcome follows a general linear model. The information of both validation samples and nonvalidation samples are used. What is more, we prove the asymptotic properties of the proposed MSELE.