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We develop a novel doubly-robust (DR) imputation framework for longitudinalstudies with monotone dropout, motivated by the informative dropout that iscommon in FDA-regulated trials for Alzheimer's disease. In this approach, themissing data are first imputed using a doubly-robust augmented inverseprobability weighting (AIPW) estimator, then the imputed completed data aresubstituted into a full-data estimating equation, and the estimate is obtainedusing standard software. The imputed completed data may be inspected andcompared to the observed data, and standard model diagnostics are available.The same imputed completed data can be used for several different estimands,such as subgroup analyses in a clinical trial, allowing for reduced computationand increased consistency across analyses. We present two specific DRimputation estimators, AIPW-I and AIPW-S, study their theoretical properties,and investigate their performance by simulation. AIPW-S has substantiallyreduced computational burden compared to many other DR estimators, at the costof some loss of efficiency and the requirement of stronger assumptions.Simulation studies support the theoretical properties and good performance ofthe DR imputation framework. Importantly, we demonstrate their ability toaddress time-varying covariates, such as a time by treatment interaction. Weillustrate using data from a large randomized Phase III trial investigating theeffect of donepezil in Alzheimer's disease, from the Alzheimer's DiseaseCooperative Study (ADCS) group.

An Efficient Doubly-robust Imputation Framework for Longitudinal Dropout, with an Application to an Alzheimer's Clinical Trial