Bayesian Modeling in Intervention Research

Based on recent publications in the Journal of the Society for Social Work and Research, the purpose of the proposed workshop is to review and demonstrate a Bayesian perspective on intervention research. Drawing on theory and prior research, a central feature of intervention research is the sequential testing of programs, where knowledge about an intervention accrues over time from small and typically simple studies to more complex randomized control trials (e.g., cluster randomized and stepped wedge studies). Based on sample sizes from traditional power estimates, intervention researchers recruit study participants and run trials. When a study is completed, statistical analyses are undertaken and recommendations are made. This is called a frequentist approach. In the analysis, the frequentist approach ignores prior knowledge on the effects of interventions and, because analyses are not conditioned on prior information, they are inefficient. A logical extension of the frequentist approach is to incorporate prior knowledge into analyses by using Bayesian modeling. In this workshop, we will present both the conceptual and technical frameworks underlying an emerging Bayesian perspective on intervention research. At the completion of the workshop, participants will have an understanding of the core features, assumptions, methods, and challenges in using Bayesian modeling in intervention research. As a part of the presentation, we will review steps in a Bayesian analysis and invite participants to analyze their own intervention data (if they have it) or data provided in the workshop.