Cluster Trials Inference With CARE

Area of Science:

• Biostatistics
• Clinical Trials Methodology
• Public Health Research

Background:

• Cluster-randomized trials (CRTs), particularly pragmatic ones, frequently display significant, often overlooked, heterogeneity in cluster sizes and structures.
• This imbalance can distort statistical inference, compromising the validity of trial results.

Purpose of the Study:

• To highlight the underappreciated impact of cluster size and structure heterogeneity in CRTs.
• To evaluate the performance of existing statistical methods under such conditions.
• To introduce a novel protocol for more robust CRT analysis.

Main Methods:

• Simulations involving reassigning treatment in real data to assess heterogeneity effects.
• Analysis of synthetic data with varying levels of imbalance.
• Comparison of current methods (e.g., targeted maximum likelihood estimation, generalized estimating equations) against proposed benchmarks.

Main Results:

• Current standard methods demonstrate suboptimal performance when faced with substantial cluster heterogeneity.
• The proposed CARE (Clarify, Apply, Refine, Evaluate) protocol provides a more reliable framework for inference.
• The CARE protocol anchors analysis in a design-based benchmark, improving credibility.

Conclusions:

• Substantial heterogeneity in cluster size and structure is a critical, yet often underestimated, issue in cluster-randomized trials.
• Existing analytical methods require re-evaluation for their suitability in the presence of significant imbalance.
• The CARE protocol offers a transparent and principled approach to enhance the credibility and comparability of CRT analyses.