  We develop a novel doubly-robust (DR) imputation framework for longitudinal
studies with monotone dropout, motivated by the informative dropout that is
common in FDA-regulated trials for Alzheimer's disease. In this approach, the
missing data are first imputed using a doubly-robust augmented inverse
probability weighting (AIPW) estimator, then the imputed completed data are
substituted into a full-data estimating equation, and the estimate is obtained
using standard software. The imputed completed data may be inspected and
compared 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 computation
and increased consistency across analyses. We present two specific DR
imputation estimators, AIPW-I and AIPW-S, study their theoretical properties,
and investigate their performance by simulation. AIPW-S has substantially
reduced computational burden compared to many other DR estimators, at the cost
of some loss of efficiency and the requirement of stronger assumptions.
Simulation studies support the theoretical properties and good performance of
the DR imputation framework. Importantly, we demonstrate their ability to
address time-varying covariates, such as a time by treatment interaction. We
illustrate using data from a large randomized Phase III trial investigating the
effect of donepezil in Alzheimer's disease, from the Alzheimer's Disease
Cooperative Study (ADCS) group.
