Research Techniques Made Simple: Network Meta-Analysis.

When making treatment decisions, it is often necessary to consider the relative efficacy and safety of multiple potential interventions. Unlike traditional pairwise meta-analysis, which allows for a comparison between two interventions by pooling head-to-head data, network meta-analysis (NMA) allows for the simultaneous comparison of more than two interventions and for comparisons to be made between interventions that have not been directly compared in a randomized controlled trial. Given these advantages, NMAs are being published in the medical literature with increasing frequency. However, there are important assumptions that researchers and knowledge users (e.g., patients, clinicians, and policy makers) must consider when conducting and evaluating an NMA: network connectivity, homogeneity, transitivity, and consistency. There are also multiple NMA outputs that researchers and knowledge users should familiarize themselves with in order to understand NMA results (e.g., network plots, mean ranks). Our goals in this article are to: (i) demonstrate how NMAs differ from pairwise meta-analyses, (ii) describe types of evidence in a NMA, (iii) explain NMA model assumptions, (iv) provide readers with an approach to interpreting a NMA, (v) discuss areas of ongoing methodological research, and (vi) provide a brief overview of how to conduct a systematic review and NMA.

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Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.