Bayesian Geostatistical Modeling for Cluster Randomized Trials

Cluster randomized trials (CRTs) offer a practical alternative for addressing logistical challenges and ensuring feasibility in community health, education, and prevention studies, even though individually-randomized controlled trials are considered the gold standard in evaluating therapeutic interventions.

Despite their utility, CRTs are often criticized for limited precision and complex modeling requirements.

Advances in robust Bayesian methods and the incorporation of spatial correlation into CRT design and analysis remain relatively underdeveloped.

This paper introduces a Bayesian geostatistical framework that models individuals nested within geographic clusters while explicitly accounting for spatial dependence.

We demonstrate that conventional non-spatial models are susceptible to underestimating uncertainty and lead to misleading inferences, whereas our spatial approach improves estimation stability, controls type I error, and enhances statistical power.

Additionally, we explore design implications that are suggested through the exploration of spatial predictive uncertainty.

Our results of simulation and real-world data application demonstrate the value and need for wider adoption of spatial methods in CRTs.