Network Meta-analysis and Diffusion

TL;DR

This paper introduces a novel method to compute the covariance matrix in network meta-analysis using diffusion matrices, avoiding matrix inversion, and links it to random walks on graphs, with visualization tools in R.

Contribution

It presents a new approach for covariance estimation in network meta-analysis leveraging diffusion matrices and connects it to graph random walks, with practical R tools.

Findings

Covariance matrix can be obtained without matrix inversion.

The method extends to the hat matrix in regression analysis.

Provides visualization tools implemented in R.

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.