ABSTRACT We show that cluster‐randomized trials—especially pragmatic designs—often exhibit substantial (but underappreciated in practice) heterogeneity in cluster sizes and structures, distorting inference. Our simulations—reassigning treatment in real data and varying imbalance in synthetic data—show that currently recommended methods (such as targeted maximum likelihood estimation and small‐sample corrected generalized estimating equations) are not optimized to this challenge. We propose the CARE (Clarify, Apply, Refine, Evaluate) protocol, which anchors inference in a design‐based benchmark and provides a principled pathway for incorporating assumption‐rich methods—making trial analysis more credible, transparent, and directly comparable across studies.
Alexeev et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: