A new semiparametric procedure for matched case-control studies with missing covariates

Samiran Sinha and Suojin Wang

Journal of Nonparametric Statistics, 2009, vol. 21, issue 7, 889-905

Abstract: In this paper, we propose an easy-to-use semiparametric method for analysing matched case-control data when one of the covariates of interest is partially missing. Missing covariate information in matched case-control studies may create bias and reduce efficiency of the parameter estimates. In order to cope with this situation we consider 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 theory of the proposed estimator is investigated and the asymptotic normality is obtained. A simulation study is conducted 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.

Date: 2009
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DOI: 10.1080/10485250903019523

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