Tootfinder

Bayesian non-parametric lumping and splitting of nodes in Network Meta-Analysis under heterogeneity
Timothy Disher, Chris Cameron, Brian Hutton
https://arxiv.org/abs/2506.22154

Bayesian non-parametric lumping and splitting of nodes in Network Meta-Analysis under heterogeneity
Network meta-analysis (NMA) synthesizes evidence for multiple treatments, but decisions on node formation can have important statistical implications including bias or inflated uncertainty. Existing data-driven methods often lack flexibility or fail to fully account for node uncertainty and adjust for between-trial heterogeneity simultaneously. We introduce a Bayesian non-parametric framework using a Dirichlet process prior with a regularized horseshoe base measure. This data-driven approach al…