Evidence map›Paper›PMID 30342086›Full record

ArticleJournal of clinical epidemiology2019

Borrowing of strength from indirect evidence in 40 network meta-analyses.

Lifeng Lin, Aiwen Xing, Michael J Kofler, Mohammad Hassan Murad

Abstract readNetwork Meta-Analysis
In one paragraph

Article in Journal of clinical epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
–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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
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  11. On evidence cycles in network meta-analysis.Statistics and its interface · 2020
    Article
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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

4 authors.

Lifeng LinDepartment of Statistics, Florida State University, Tallahassee, FL 32306, USA. Electronic address: linl@stat.fsu.edu.
Aiwen XingDepartment of Statistics, Florida State University, Tallahassee, FL 32306, USA.
Michael J KoflerDepartment of Psychology, Florida State University, Tallahassee, FL 32306, USA.
Mohammad Hassan MuradEvidence-Based Practice Center, Mayo Clinic, Rochester, MN 55905, USA.

Funding

Robust statistical methods and software for meta-analysis with outlying studiesR03HS024743 · AHRQ · UNIVERSITY OF MINNESOTA · PI CHU, HAITAO · 2017 to 2017
$98k
AHRQ HHS R03 HS024743
6 · The paper itself

Abstract

objectivesNetwork meta-analysis (NMA) is increasingly being used to synthesize direct and indirect evidence and help decision makers simultaneously compare multiple treatments. We empirically evaluate the incremental gain in precision achieved by incorporating indirect evidence in NMAs. STUDY DESIGN AND

settingWe performed both network and pairwise meta-analyses on 40 published data sets of multiple-treatment comparisons. Their results were compared using the recently proposed borrowing of strength (BoS) statistic, which quantifies the percentage reduction in the uncertainty of the effect estimate when adding indirect evidence to an NMA.

resultsWe analyzed 915 possible treatment comparisons, from which 484 (53%) had no direct evidence (BoS = 100%). In 181 comparisons with only one study contributing direct evidence, NMAs resulted in reduced precision (BoS < 0) and no appreciable improvements in precision (0 < BoS < 30%) for 104 (57.5%) and 23 (12.7%) comparisons, respectively. In 250 comparisons with at least two studies contributing direct evidence, NMAs provided increased precision with BoS ≥ 30% for 166 (66.4%) comparisons.

conclusionAlthough NMAs have the potential to provide more precise results than those only based on direct evidence, the incremental gain may reliably occur only when at least two head-to-head studies are available and treatments are well connected. Researchers should routinely report and compare the results from both network and pairwise meta-analyses.

Indexed as

Models, StatisticalBayes TheoremData Interpretation, StatisticalEvidence-Based MedicineHumansBayesian analysisBorrowing of strengthIndirect evidenceNetwork meta-analysisResearch synthesisSystematic review

Identifiers

PMID30342086
PMCPMC6338506

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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 OpenQuestion graph.