Two-Stage TMLE to Reduce Bias and Improve Efficiency in Cluster Randomized Trials

Abstract

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.

37 pages total; main text is 17 pgs with 2 figures and 3 tables; supp material is 14 pgs with 1 figure and 5 tables

Cited by in corpus (4)

• Model-robust and efficient covariate adjustment for cluster-randomized experiments
• Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials
• Recoverability of Causal Effects under Presence of Missing Data: a Longitudinal Case Study
• Statistical Analysis Plan for Health Outcomes in Phase 1 of the SEARCH-IPT Study