A semiparametric missing-data-induced intensity method for missing covariate data in individually matched case-control studies.

ABSTRACT: In individually matched case-control studies, when some covariates are incomplete, an analysis based on the complete data may result in a large loss of information both in the missing and completely observed variables. This usually results in a bias and loss of efficiency. In this article, we propose a new method for handling the problem of missing covariate data based on a missing-data-induced intensity approach when the missingness mechanism does not depend on case-control status and show that this leads to a generalization of the missing indicator method. We derive the asymptotic properties of the estimates from the proposed method and, using an extensive simulation study, assess the finite sample performance in terms of bias, efficiency, and 95% confidence coverage under several missin

SUBMITTER: Gebregziabher M 

PROVIDER: S-EPMC2910202 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature