bayesian-meta-analysis

Bayesian meta-analysis using the NNHM model involves specifying priors for the effect size mu and heterogeneity tau, configuring MCMC parameters, and running simulations to obtain posterior distributions. This workflow guides model specification, prior selection, and execution steps.

Reporting Bayesian meta-analysis results aligned with PRISMA 2020 requires using extended reporting templates that cover posterior summaries, prediction intervals, and prior sensitivity analyses to ensure transparent documentation of systematic reviews.

Checking MCMC convergence diagnostics for Bayesian meta-analysis involves evaluating posterior summaries and trace plots after configuring the MCMC settings. This process ensures the simulations have stabilized before interpreting the final heterogeneity and uncertainty results.

Prior sensitivity analyses are needed in systematic reviews with small-study settings to test how robust posterior results are against different specifications of priors for mu and tau. They quantify uncertainty and validate model stability.

Yes, you can use Bayesian meta-analysis to quantify heterogeneity and uncertainty in small-study settings. By applying the NNHM model and appropriate priors, researchers can derive posterior summaries and prediction intervals for robust effect estimation.