A Bayesian Perspective on Intervention Research

Using Prior Information in the Development of Social and Health Programs

Dados Bibliográficos

ID12987152
AutoresDing‐Geng Chen (0000-0002-3199-8665, University of North Carolina at Chapel Hill, autor correspondente), Mark W Fraser (0000-0001-6289-7798, University of North Carolina at Chapel Hill)
Ano2017
Volume8
Fascículo3
Páginas441-456
Data de publicação2017-07-12
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoJournal of the Society for Social Work and Research (JOURNAL)
Identificadores do periódicoISSN: 1948-822X • E-ISSN: 2334-2315
EditoraUniversity of Chicago Press (PUBLISHER • US)
DOI10.1086/693432
OpenAlexW2736013191
IdiomaEN
Citações recebidas6
Referências citadas18

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). We determined the trial sample size by a power analysis with a Type I error probability 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

Bayesian inference · Bayesian probability · Bayesian statistics · Econometrics · Frequentist inference · Frequentist probability · Machine learning · Perspective (graphical · Sample size determination · Statistical hypothesis testing · Statistical power · Statistics · Type I and type II errors · Advanced Causal Inference Techniques · Behavioral and Psychological Studies · Computer Science · Health Policy Implementation Science · Mathematics · Artificial Intelligence