Analysis of rolling group therapy data using conditionally autoregressive priors

TL;DR

This paper introduces a hierarchical Bayesian model with conditionally autoregressive priors to analyze complex correlation structures in rolling group therapy data, enhancing understanding of client outcomes.

Contribution

It presents a novel Bayesian approach that models interrelated client outcomes in rolling therapy groups, improving analysis accuracy over previous methods.

Findings

Improved estimation of variance parameters.

Enhanced modeling of correlation among clients.

Broader applicability to various group therapy settings.

Abstract

Group therapy is a central treatment modality for behavioral health disorders such as alcohol and other drug use (AOD) and depression. Group therapy is often delivered under a rolling (or open) admissions policy, where new clients are continuously enrolled into a group as space permits. Rolling admissions policies result in a complex correlation structure among client outcomes. Despite the ubiquity of rolling admissions in practice, little guidance on the analysis of such data is available. We discuss the limitations of previously proposed approaches in the context of a study that delivered group cognitive behavioral therapy for depression to clients in residential substance abuse treatment. We improve upon previous rolling group analytic approaches by fully modeling the interrelatedness of client depressive symptom scores using a hierarchical Bayesian model that assumes a conditionally autoregressive prior for session-level random effects. We demonstrate improved performance using our method for estimating the variance of model parameters and the enhanced ability to learn about the complex correlation structure among participants in rolling therapy groups. Our approach broadly applies to any group therapy setting where groups have changing client composition. It will lead to more efficient analyses of client-level data and improve the group therapy research community's ability to understand how the dynamics of rolling groups lead to client outcomes.