Visualizing the assumptions of network meta‐analysis

Network meta‐analysis (NMA) incorporates all available evidence into a general statistical framework for comparing multiple treatments. Standard NMAs make three major assumptions, namely homogeneity, similarity, and consistency, and violating these assumptions threatens an NMA's validity. In th...

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Bibliographic Details
Published in:Research synthesis methods 2024-11, Vol.15 (6), p.1175-1182
Main Authors: Tu, Yu‐Kang, Lai, Pei‐Chun, Huang, Yen‐Ta, Hodges, James
Format: Article
Language:English
Subjects:
Algorithms
Computer Graphics
consistency
Data Interpretation, Statistical
exchangeability
Homogeneity
Humans
Meta-analysis
Models, Statistical
Network Meta-Analysis
Qualitative analysis
Reproducibility of Results
Research Design
Shock, Septic - drug therapy
Shock, Septic - therapy
similarity
Steroids - therapeutic use
transitivity
Treatment Outcome
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Description
Summary:Network meta‐analysis (NMA) incorporates all available evidence into a general statistical framework for comparing multiple treatments. Standard NMAs make three major assumptions, namely homogeneity, similarity, and consistency, and violating these assumptions threatens an NMA's validity. In this article, we suggest a graphical approach to assessing these assumptions and distinguishing between qualitative and quantitative versions of these assumptions. In our plot, the absolute effect of each treatment arm is plotted against the level of effect modifiers, and the three assumptions of NMA can then be visually evaluated. We use four hypothetical scenarios to show how violating these assumptions can lead to different consequences and difficulties in interpreting an NMA. We present an example of an NMA evaluating steroid use to treat septic shock patients to demonstrate how to use our graphical approach to assess an NMA's assumptions and how this approach can help with interpreting the results. We also show that all three assumptions of NMA can be summarized as an exchangeability assumption. Finally, we discuss how reporting of NMAs can be improved to increase transparency of the analysis and interpretability of the results.
ISSN:1759-2879
1759-2887
1759-2887
DOI:10.1002/jrsm.1760