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A Path-Based Approach for Detecting and Assessing Inconsistency in Network Meta-Analysis.

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Title: A Path-Based Approach for Detecting and Assessing Inconsistency in Network Meta-Analysis.
Authors: Tahmasebi NR; Institute for Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.; Institute for Evidence in Medicine (IfEM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany., Davies AL; Bristol Medical School, University of Bristol, Bristol, UK., Papakonstantinou T; Institute for Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.; Laboratory of Hygiene, Social and Preventive Medicine and Medical Statistics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece., Rücker G; Institute for Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany., Nikolakopoulou A; Institute for Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.; Laboratory of Hygiene, Social and Preventive Medicine and Medical Statistics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Source: Statistics in medicine [Stat Med] 2026 Oct; Vol. 45 (23-24), pp. e70752.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
Imprint Name(s): Original Publication: Chichester ; New York : Wiley, c1982-
MeSH Terms: Network Meta-Analysis as Topic*, Humans ; Models, Statistical ; Data Interpretation, Statistical
Abstract: Network Meta-Analysis (NMA) is a statistical technique for synthesizing evidence from various sources to compare multiple interventions. NMA relies on integrating both direct evidence from head-to-head comparisons and indirect evidence from different paths that link treatments through common comparators. A key aspect is evaluating consistency between direct and indirect sources. Existing methods to detect inconsistency, although widely used, have limitations. For example, they do not account for differences between indirect sources or cannot estimate inconsistency when direct evidence is absent. In this paper, we introduce a path-based approach that explores all sources of evidence without separating direct and indirect. We introduce a measure based on the square of differences to quantitatively capture inconsistency, and propose a Netpath plot to visualize inconsistencies between various paths. Moreover, it enables the evaluation of inconsistency even for treatment comparisons lacking direct evidence as long as at least two independent paths of evidence are available. We provide an implementation of our path-based method within the netmeta R package. Via application to fictional and real-world examples, we show that our method is able to detect and visualize inconsistency between multiple paths of evidence that would otherwise be masked by considering all indirect sources together. The path-based approach therefore provides a more comprehensive evaluation of inconsistency within a network of treatments.
(© 2026 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)
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Grant Information: NI 2226/1-1 Deutsche Forschungsgemeinschaft; Project-ID 499552394 SFB 1597 Deutsche Forschungsgemeinschaft; EP/Y007905/1 Engineering and Physical Sciences Research Council
Contributed Indexing: Keywords: incoherence; inconsistency; mixed‐treatment comparison; network meta‐analysis; paths
Entry Date(s): Date Created: 20261016 Date Completed: 20261016 Latest Revision: 20261020
Update Code: 20261020
PubMed Central ID: PMC13672679
DOI: 10.1002/sim.70752
PMID: 42881364
Database: MEDLINE
Description
ISSN:1097-0258
DOI:10.1002/sim.70752