Network meta-analysis and diffusion
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.
Authors
- Gerta Rücker (ORCID: https://orcid.org/0000-0002-2192-2560)
- Annabel L. Davies (ORCID: https://orcid.org/0000-0003-2320-7701)
- Guido Schwarzer (ORCID: https://orcid.org/0000-0001-6214-9087)
Institutions
- University of Freiburg (DE)
- University Medical Center Freiburg (DE)
- University of Bristol (GB)
Publication Details
- Journal
- Research Synthesis Methods
- Published
- 2026-10-15
- DOI
- https://doi.org/10.1017/rsm.2026.10123
- Primary Topic
- Meta-analysis and systematic reviews
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Engineering and Physical Sciences Research Council