Evidence map›Paper›PMID 41626954›Full record

ArticleResearch synthesis methods2025

Tipping point analysis in network meta-analysis.

Zheng Wang, Thomas A Murray, Wenshan Han, Lifeng Lin, Lianne K Siegel, Haitao Chu

Abstract read
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In one paragraph

Article in Research synthesis methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zheng WangDepartment of Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, USA.ORCID https://orcid.org/0000-0002-1083-5269
Thomas A MurrayDivision of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Wenshan HanDepartment of Population and Community Health, University of North Texas Health Science Center, Fort Worth, TX, USA.
Lifeng LinDepartment of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA.ORCID https://orcid.org/0000-0002-3562-9816
Lianne K SiegelDivision of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA.ORCID https://orcid.org/0000-0001-9440-9146
Haitao ChuDivision of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA.ORCID https://orcid.org/0000-0003-0932-598X

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UM1TR004405 · UNIVERSITY OF MINNESOTA · 2025 to 2025
$7.7M
Joint modeling of continuous and binary data in meta-analysisR03MH128727 · NIMH · FLORIDA STATE UNIVERSITY · PI Lifeng Lin · 2023 to 2023
$28k
NCATS NIH HHS UL1 TR002494NCATS NIH HHS UM1 TR004405NIMH NIH HHS R03 MH128727NLM NIH HHS R01 LM012982
6 · The paper itself

Abstract

Network meta-analysis (NMA) enables simultaneous assessment of multiple treatments by combining both direct and indirect evidence. While NMAs are increasingly important in healthcare decision-making, challenges remain due to limited direct comparisons between treatments. This data sparsity complicates the accurate estimation of correlations among treatments in arm-based NMA (AB-NMA). To address these challenges, we introduce a novel sensitivity analysis tool tailored for AB-NMA. This study pioneers a tipping point analysis within a Bayesian framework, specifically targeting correlation parameters to assess their influence on the robustness of conclusions about relative treatment effects. The analysis explores changes in the conclusion based on whether the 95% credible interval includes the null value (referred to as the

Indexed as

Network Meta-Analysis as TopicAlgorithmsBayes TheoremComputer SimulationData Interpretation, StatisticalHumansModels, StatisticalReproducibility of ResultsResearch Designcorrelation between multiple treatmentsnetwork meta-analysisrobustness of research conclusionsensitivity analysistipping point analysis

Identifiers

PMID41626954
PMCPMC12527527

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.