New Causal Inference Methods for Cluster Randomized Trials with Post-Randomization Selection Bias [Methods Study], United States, 2019-2023

Cluster randomized trials, or CRTs, are studies that compare treatments across different groups of patients, or clusters. An example of a cluster is people who receive care at one clinic.
To reduce bias in CRT results, researchers assign clusters by chance to different treatments. But what happens after they assign treatment can lead to differences across clusters and bias the results. For example, patients who visit clinics assigned to a treatment may be older than patients who visit clinics not assigned to that treatment. Current statistical methods for analyzing data from CRTs don't work well to account for these differences.
In this study, the research team developed new methods to account for differences across clusters after treatment assignment.