Efficacy studies of malaria treatments in Africa: efficient estimation with missing indicators of failure

The effect of missing data in causal inference problems is widely recognized. In malaria drug efficacy studies, it is often difficult to distinguish between new and old infections after treatment, resulting in indeterminate outcomes. Methods that adjust for possible bias from missing data include a variety of imputation procedures (extreme case analysis, hot-deck, single and multiple imputation), weighting methods, and likelihood based methods (data augmentation, EM procedures and their extensions). In this article, we focus our discussion on multiple imputation and two weighting procedures (the inverse probability weighted and the doubly robust (DR) extension), comparing the methods' applicability to the efficient estimation of malaria treatment effects. Simulation studies indicate that DR estimators are generally preferable because they offer protection to misspecification of either the outcome model or the missingness model. We apply the methods to analyze malaria efficacy studies from Uganda.