In network meta-analysis, most of the information comes from indirect evidence: empirical study

Objectives: Network meta-analysis (NMA) may produce more precise estimates of treatment effects than pairwise meta-analysis. We examined the relative contribution of network paths of different lengths to estimates of treatment effects. Study Design and Setting: We analyzed 213 published NMAs. We categorized network shapes according to the presence or absence of at least one closed loop (nonstar or star network) and derived the graph density, radius, and diameter. We identified paths of different lengths and calculated their percentage contribution to each NMA effect estimate, based on their contribution matrix. Results: Among the 213 NMAs included in analyses, 33% of the information came from paths of length 1 (direct evidence), 47% from paths of length 2 (indirect paths with one intermediate treatment) and 20% from paths of length 3. The contribution of paths of different lengths depended on the size of networks, presence of closed loops, and graph radius, density, and diameter. Longer paths contribute more as the number of treatments and loops and the graph radius and diameter increase. Conclusion: The contribution of different paths depends on the size and structure of networks, with important implications for assessing the risk of bias and confidence in NMA results. (C) 2020 The Authors. Published by Elsevier Inc.