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Abstract:
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Data with missing covariate values but fully observed binary outcomes are an important subset of the missing data challenge. Common approaches are complete case analysis (CCA) and multiple imputation (MI). For MI involving logistic regression models it is also important to consider several missing not at random (MNAR) conditions under which CCA is asymptotically unbiased and, as we show, MI is also valid in some cases. We compare the performance of several machine learning and parametric MI methods under a fully-conditional-specification framework (MI-FCS). Our simulation includes five scenarios involving MCAR, MAR and MNAR under predictable and non-predictable conditions, where ‘predictable’ indicates missingness is not associated with the outcome. We build on previous results in the literature to show MI and CCA can both produce unbiased results under more conditions than some analysts may realize. When both approaches were valid, we found that MI-FCS was at least as good as CCA in terms of estimated bias and coverage, and was superior when missingness involved a categorical covariate.
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