  Missing outcome data is one of the principal threats to the validity of
treatment effect estimates from randomized trials. The outcome distributions of
participants with missing and observed data are often different, which
increases the risk of bias. Causal inference methods may aid in reducing the
bias and improving efficiency by incorporating baseline variables into the
analysis. In particular, doubly robust estimators incorporate estimates of two
nuisance parameters: the outcome regression and the missingness mechanism, to
adjust for differences in the observed and unobserved groups that can be
explained by observed covariates. Such nuisance parameters are traditionally
estimated using parametric models, which generally preclude consistent
estimation, particularly in moderate to high dimensions. Recent research on
missing data has focused on data-adaptive estimation of the nuisance parameters
in order to achieve consistency, but the large sample properties of such
estimators are poorly understood. In this article we discuss a doubly robust
estimator that is consistent and asymptotically normal (CAN) under
data-adaptive consistent estimation of the outcome regression or the
missingness mechanism. We provide a formula for an asymptotically valid
confidence interval under minimal assumptions. We show that our proposed
estimator has smaller finite-sample bias compared to standard doubly robust
estimators. We present a simulation study demonstrating the enhanced
performance of our estimators in terms of bias, efficiency, and coverage of the
confidence intervals. We present the results of an illustrative example: a
randomized, double-blind phase II/III trial of antiretroviral therapy in
HIV-infected persons, and provide R code implementing our proposed estimators.
