28: Improved Covariate-Constrained Randomization Strategies to Better Balance Baseline Covariates

Music City Center 

Cluster randomized trials are often used to evaluate diverse types of interventions in which groups of individuals are randomized, and the interventions are delivered at the cluster level. These types of randomized trials do not always effectively balance cluster- and individual-level characteristics, resulting in a higher risk of bias. We implemented covariate-constrained randomization (CCR) in a longitudinal cluster-randomized de-implementation trial with over 40 hospitals enrolled to evaluate two de-implementation strategies for reducing overuse of continuous pulse oximetry monitoring in children with bronchiolitis. CCR was performed using the baseline over-monitoring rate of each hospital and two other hospital characteristics, which were strong independent predictors of outcome. The current metrics for balance in CCR only consider the mean levels of covariates between arms, ignoring the full distributions of covariates. We examine the impact of outliers in covariates, particularly in combination with a small number of clusters on the randomization. We propose several strategies, including a stratified randomization procedure, to improve the covariate balance at baseline.

Covariate-Constrained Randomization

Cluster Randomized Trials

Implementation Science 

Section on Statistics in Epidemiology