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Group therapy is a central treatment modality for behavioral health disorderssuch as alcohol and other drug use (AOD) and depression. Group therapy is oftendelivered under a rolling (or open) admissions policy, where new clients arecontinuously enrolled into a group as space permits. Rolling admissionspolicies result in a complex correlation structure among client outcomes.Despite the ubiquity of rolling admissions in practice, little guidance on theanalysis of such data is available. We discuss the limitations of previouslyproposed approaches in the context of a study that delivered group cognitivebehavioral therapy for depression to clients in residential substance abusetreatment. We improve upon previous rolling group analytic approaches by fullymodeling the interrelatedness of client depressive symptom scores using ahierarchical Bayesian model that assumes a conditionally autoregressive priorfor session-level random effects. We demonstrate improved performance using ourmethod for estimating the variance of model parameters and the enhanced abilityto learn about the complex correlation structure among participants in rollingtherapy groups. Our approach broadly applies to any group therapy setting wheregroups have changing client composition. It will lead to more efficientanalyses of client-level data and improve the group therapy researchcommunity's ability to understand how the dynamics of rolling groups lead toclient outcomes.

Analysis of rolling group therapy data using conditionally autoregressive priors