Sec.2-Ch.2-Subsec.8：A Practical Guide to Bayesian Diagnostic Meta-Analysis in R（III）
Implementing Posterior Predictive Surfaces, Conflict Weights, and Comparative Visualizations in the R Environment
Bayesian methods have become increasingly popular in systematic reviews and meta-analyses of diagnostic test accuracy because of their flexibility in handling small samples, sparse data, and high heterogeneity. Compared with classical (frequentist) approaches, Bayesian analysis not only provides point estimates but also fully characterizes uncertainty and its distribution.
In R, the bamdit package offers an integrated framework for Bayesian bivariate random-effects meta-analysis of diagnostic tests. It enables complex model fitting (via MCMC methods such as JAGS) and provides rich visualization tools that help researchers interpret model results, explore heterogeneity, and identify potential conflicting evidence.
