Network Meta-analysis and Diffusion
Methodology
2026-04-20 v1
Abstract
We show that the covariance matrix of the treatment effect estimates in a network meta-analysis can be obtained without matrix inversion using a geometric series of diffusion matrices. This property extends to the hat matrix and provides a connection between parameter estimation in regression analysis and random walks on the network graph. We also provide a number of visualization tools implemented in R.
Keywords
Cite
@article{arxiv.2604.16221,
title = {Network Meta-analysis and Diffusion},
author = {Gerta Rücker and Annabel L. Davies and Guido Schwarzer},
journal= {arXiv preprint arXiv:2604.16221},
year = {2026}
}
Comments
19 pages, 8 figures