loading . . . Covariate adjustment in cluster randomised trials: a practical guide Covariate adjustment can offer several potential benefits in the analysis of cluster randomised trials. These benefits include increasing statistical precision (ie, narrowing width of confidence intervals), as well as potentially reducing any bias arising from differential identification and recruitment across arms or missing outcome data. This article outlines a guideline for how to choose covariates to include in a prespecified adjustment plan for such trials. Recommendations include adjusting for covariates that have been included in any restricted randomisation; and adjusting for a prespecified set of covariates thought to be prognostic of the outcome, differential recruitment, or outcome missingness. When the prevalence of missing covariate or outcome data are non-negligible, a missing data technique such as multiple imputation (allowing for clustering), cluster mean imputation, or the missing indicator method, is recommended. In a case study, the proposed prespecified analysis plan includes adjustment for minimisation variables as well as four covariates thought to be prognostic of the outcome and potentially related to unblinded identification of participants after randomisation.
Cluster randomised trials (CRTs) are a vital research design, especially when interventions target groups rather than individuals.1234 In CRTs, entire clusters—such as schools, hospitals, or communities—are randomised to different interventions, making them particularly useful in public health, education, and health systems research. While CRTs are invaluable, they come with specific methodological challenges. Because individuals within a cluster may respond similarly, statistical analyses must account for this intracluster correlation to generate valid results.56
Covariate adjustment can have an important role in the analysis of CRTs.7 Firstly, in trials that use some form of restricted randomisation (eg, stratification or minimisation), covariate adjustment can increase statistical precision.8910 Thus, similarly to individually randomised trials, adjusting for any stratification factors can lead to standard errors that are (appropriately) smaller … https://www.bmj.com/content/391/bmj-2025-084194
