A Gentle Introduction to Bayesian Network Meta-Analysis Using an Automated R Package.

Area of Science:

• Psychology and Social Sciences
• Biostatistics
• Evidence-Based Practice

Background:

• Standard meta-analysis synthesizes evidence from multiple studies on a single comparison.
• Network meta-analysis (NMA) extends this by synthesizing evidence from multiple direct and indirect comparisons, forming a network of interventions.
• Bayesian approaches offer a robust framework for NMA, enabling comprehensive analysis and uncertainty quantification.

Purpose of the Study:

• To introduce fundamental concepts of Bayesian network meta-analysis (BNMA) to researchers in psychology and social sciences.
• To provide a practical guide for conducting and reporting BNMA in psychological research.
• To highlight key considerations and potential pitfalls in BNMA application.

Main Methods:

• Utilizing a Bayesian framework to estimate posterior probability distributions for relative treatment effects.
• Discussing essential BNMA concepts: homogeneity, consistency, fixed/random effects models, prior specification, and model fit.
• Demonstrating BNMA using an automated R package with a real-world dataset of psychological interventions.

Main Results:

• BNMA allows for the estimation of relative treatment effects and quantification of parameter uncertainty.
• The method facilitates the ranking of all treatments within the evidence network.
• Direct and indirect evidence integration provides valuable guidance for policy makers and clinicians.

Conclusions:

• BNMA is a powerful tool for synthesizing complex evidence networks in psychology and social sciences.
• Proper application requires careful consideration of assumptions, model specification, and interpretation.
• This approach enhances evidence-based decision-making by providing comprehensive treatment comparisons.