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, although CCA gives consistent estimates, it does not make use of all observed information. We therefore propose an augmented CCA approach which makes the same conditional independence assumption for missingness as CCA, but which improves efficiency through specification of an additional model for the probability of missingness, given the fully observed variables. The new method is evaluated using simulations and illustrated through application to data on reported alcohol consumption and blood pressure from the US National Health and Nutrition Examination Survey, in which data are likely MNAR independent of outcome.
Original language  English 

Pages (fromto)  719730 
Number of pages  12 
Journal  Biostatistics 
Volume  15 
Issue number  4 
Early online date  6 Jun 2014 
DOIs  
Publication status  Published  1 Oct 2014 
Keywords
 Alcohol Drinking
 Blood Pressure
 Data Interpretation, Statistical
 Humans
 Models, Statistical
 Journal Article
 Research Support, NonU.S. Gov't
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Jonathan Bartlett
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