Dr. Suojin Wang, Department of Statistics at the Texas A&M University

A New Semiparametric Procedure for Matched Case-Control Studies with Missing Covariate Data

In this talk we consider an easy-to-use semiparametric method for analyzing matched case-control data when one of the covariates of interest is partially missing. Missing covariate information in matched case-control study may create bias and reduce efficiency of the parameter estimates. In order to cope with this situation we propose a robust approach which is comprised of estimating some functionals of the distribution of the partially missing covariate using a kernel regression technique in a conditional likelihood framework. The large sample properties of the proposed estimator are investigated and the asymptotic normality is obtained. A simulation study is carried out to assess the performance of the proposed method in terms of robustness and efficiency. The proposed method is also applied to a real dataset which motivates this work.