How should parallel cluster randomized trials with a baseline period be analyzed? A survey of estimands and common estimators
Kenneth Menglin Lee, Fan Li

TL;DR
This paper investigates the analysis of parallel cluster randomized trials with a baseline period, defining estimands, deriving estimator convergence, and comparing robustness of models through simulations and real data re-analysis.
Contribution
It introduces and compares estimators for PB-CRTs with informative cluster sizes, highlighting the robustness of exchangeable mixed-effects models in practical analysis.
Findings
Unweighted and fixed-effects estimators are consistent for key estimands.
Exchangeable mixed-effects models show surprising robustness to bias.
Empirical results support theoretical findings and practical implications.
Abstract
The parallel cluster randomized trial with baseline (PB-CRT) is a common variant of the standard parallel cluster randomized trial (P-CRT). We define two natural estimands in the context of PB-CRTs with informative cluster sizes, the participant-average treatment effect (pATE) and cluster-average treatment effect (cATE), to address participant and cluster-level hypotheses. In this work, we theoretically derive the convergence of the unweighted and inverse cluster-period size weighted (i.) independence estimating equation, (ii.) fixed-effects model, (iii.) exchangeable mixed-effects model, and (iv.) nested-exchangeable mixed-effects model treatment effect estimators in a PB-CRT with continuous outcomes. Overall, we theoretically show that the unweighted and weighted independence estimating equation and fixed-effects model yield consistent estimators for the pATE and cATE estimands.…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Statistical Methods and Inference · Statistical Methods and Bayesian Inference
