Improving upon the efficiency of complete case analysis when covariates are MNAR.

ABSTRACT: Missing values in covariates of regression models are a pervasive problem in empirical research. Popular approaches for analyzing partially observed datasets include complete case analysis (CCA), multiple imputation (MI), and inverse probability weighting (IPW). In the case of missing covariate values, these methods (as typically implemented) are valid under different missingness assumptions. In particular, CCA is valid under missing not at random (MNAR) mechanisms in which missingness in a covariate depends on the value of that covariate, but is conditionally independent of outcome. In this paper, we argue that in some settings such an assumption is more plausible than the missing at random assumption underpinning most implementations of MI and IPW. When the former assumption holds, altho

SUBMITTER: Bartlett JW 

PROVIDER: S-EPMC4173105 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature