A0458
Title: Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials
Authors: Guangyu Tong - Yale University (United States) [presenting]
Abstract: Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster-randomized trials with continuous outcomes. Models fitted in the Bayesian setting with hierarchical variance structure are proposed to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings. The model is illustrated with the Kerala Diabetes Prevention Program study, in which heterogeneous variances and intraclass correlation coefficients are identified across clusters, and cluster-level characteristics are examined associated with such heterogeneity.
