Shortest path or random walks? A framework for path weights in network meta-analysis

TL;DR

This paper introduces a comprehensive framework for quantifying the contributions of paths in network meta-analysis, comparing shortest path and random walk methods, and recommending shortest path for complex networks due to efficiency.

Contribution

The paper develops a general linear framework for path contributions in NMA, unifies existing methods, and evaluates their performance, proposing shortest path as the preferred approach for complex networks.

Findings

Shortest path is faster and less variable than random walk in complex networks.

Both methods minimize the sum of absolute path contributions.

The framework can address broader questions in network meta-analysis.

Abstract

Quantifying the contributions, or weights, of comparisons or single studies to the estimates in a network meta-analysis (NMA) is an active area of research. We extend this to the contributions of paths to NMA estimates. We present a general framework, based on the path-design matrix, that describes the problem of finding path contributions as a linear equation. The resulting solutions may have negative coefficients. We show that two known approaches, called shortestpath and randomwalk, are special solutions of this equation, and both meet an optimization criterion, as they minimize the sum of absolute path contributions. In general, there is an infinite space of solutions, which can be identified using the generalized inverse (Moore-Penrose pseudoinverse). We consider two further special approaches. For complex networks we find that shortestpath is superior with respect to run time and variability, compared to the other approaches, and is thus recommended in practice. The path-weights framework also has the potential to answer more general research questions in network meta-analysis.