  Cluster randomized trials (CRTs) randomly assign an intervention to groups of
individuals (e.g., clinics or communities) and measure outcomes on individuals
in those groups. While offering many advantages, this experimental design
introduces challenges that are only partially addressed by existing analytic
approaches. First, outcomes are often missing for some individuals within
clusters. Failing to appropriately adjust for differential outcome measurement
can result in biased estimates and inference. Second, CRTs often randomize
limited numbers of clusters, resulting in chance imbalances on baseline outcome
predictors between arms. Failing to adaptively adjust for these imbalances and
other predictive covariates can result in efficiency losses. To address these
methodological gaps, we propose and evaluate a novel two-stage targeted minimum
loss-based estimator (TMLE) to adjust for baseline covariates in a manner that
optimizes precision, after controlling for baseline and post-baseline causes of
missing outcomes. Finite sample simulations illustrate that our approach can
nearly eliminate bias due to differential outcome measurement, while existing
CRT estimators yield misleading results and inferences. Application to real
data from the SEARCH community randomized trial demonstrates the gains in
efficiency afforded through adaptive adjustment for baseline covariates, after
controlling for missingness on individual-level outcomes.
