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Network meta-analysis and diffusion

delete2026-10-17
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OA
AI
G
Gerta Rücker *
A
Annabel L. Davies
G
Guido Schwarzer
DOI：10.1017/rsm.2026.10123delete
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Abstract

Abstract

En 中文
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:
covariance
diffusion
geometric series
hat matrix
network meta-analysis
random walk

Journal

Research Synthesis Methods cover
Research Synthesis Methods
IF:
6.1
Papers:
842
Citations:
8.7K

Organization

U
University of Bristol
Scholars:
720
Papers: 306
Citations: 0
M
medical center university of freiburg
Scholars:
73
Papers: 14
Citations: 0
Cited Papers

Cited Papers

No cited papers available