Mark Fraser and Din Chen will be presenting a topic on Bayesian intervention research, which they presented at last week’s SSWR with a lot of interest.
**Please RSVP to Brenda Vawter by Tuesday, January 24th, if you plan to attend this February 1st CSDC luncheon presentation. Below is the abstract for the presentation:
- CSDC luncheon
- Wednesday, February 1, 2017
- 12:15 – 1:45 p.m.
- UNC School of Social Work, Room 300
A Bayesian Perspective on Intervention Research:
Using Prior Information in the Sequential Development of Social and Health Programs
Din Chen and Mark Fraser
Abstract
Objective: By presenting a simulation study that compares Bayesian and classical frequentist approaches to research design, this paper describes and demonstrates a Bayesian perspective on intervention research. Method: Using hypothetical pilot-study data where an effect size of 0.2 had been observed, we designed a 2-arm trial intended to compare an intervention with a control condition (e.g., usual services). The sample size for the trial was determined by a power analysis with a probability of a Type-I error of 2.5% (1-sided) at 80% power. Following a Monte-Carlo computational algorithm, we simulated 1 million outcomes for this study, and then compared the performance of the Bayesian perspective with the performance of the frequentist analytic perspective. Treatment effectiveness was assessed using a frequentist t test and an empirical Bayesian t test. Statistical power was calculated as the criterion for comparison of the 2 approaches to analysis. Results: In the simulations, the classical frequentist t test yielded 80% power as designed. However, the Bayesian approach yielded 92% power. Conclusion: Holding sample size constant, a Bayesian analytic approach can improve power in intervention research. A Bayesian approach may also permit smaller samples holding power constant. Using a Bayesian analytic perspective could reduce design demands in the developmental experimentation that typifies intervention research.