Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values

Abstract

When missing data occur in one or more covariates in a regression model, multiple imputation (MI) is widely advocated as an improvement over complete-case analysis (CC). We use theoretical arguments and simulation studies to compare these methods with MI implemented under a missing at random assumption.When data are missing completely at random, both methods have negligible bias, and MI is more efficient than CC across a wide range of scenarios. For other missing data mechanisms, bias arises in one or both methods. In our simulation setting, CC is biased towards the null when data are missing at random. However, when missingness is independent of the outcome given the covariates, CC has negl..