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

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. 2009 Taylor & Francis.

authors

• Sinha, Samiran

published proceedings

• JOURNAL OF NONPARAMETRIC STATISTICS

author list (cited authors)

• Sinha, S., & Wang, S.

citation count

• 2

complete list of authors

• Sinha, Samiran||Wang, Suojin

publication date

• October 2009

publisher

• Taylor & Francis  Publisher

published in

• Journal of Nonparametric Statistics  Journal

keywords

• Conditional Logistic Regression
• Estimating Equation
• Kernel Regression
• Matched Case-control Study
• Missing At Random

Digital Object Identifier (DOI)

• 10.1080/10485250903019523

start page

• 889

end page

• 905

volume

• 21

issue

• 7

URL

• http://dx.doi.org/10.1080/10485250903019523