Cluster Trials Inference With CARE

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.