Semiparametric approach for non-monotone missing covariates in a parametric regression model.

ABSTRACT: Missing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random. In this article, we propose a semiparametric method for handling non-monotone patterns of missing data. The proposed method relies on the assumption that the missingness mechanism of a variable does not depend on the missing variable itself but may depend on the other missing variables. This mechanism is somewhat less general than the completely non-ignorable mechanism but is sometimes more flexible than the missing at random mechanism w

SUBMITTER: Sinha S 

PROVIDER: S-EPMC4061254 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature