Evidence map›Paper›PMID 24466222›Full record

ArticlePloS one2014

Characteristics of networks of interventions: a description of a database of 186 published networks.

Adriani Nikolakopoulou, Anna Chaimani, Areti Angeliki Veroniki, Haris S Vasiliadis, Christopher H Schmid, Georgia Salanti

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 3 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

56 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Tipping point analysis in network meta-analysis.Research synthesis methods · 2025
    Article
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  10. Review
  11. The development of network meta-analysis.Journal of the Royal Society of Medicine · 2022
    Article
  12. A Revised Framework to Evaluate the Consistency Assumption Globally in a Network of Interventions.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  13. Network Meta-Analysis Techniques for Synthesizing Prevention Science Evidence.Prevention science : the official journal of the Society for Prevention Research · 2022
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. 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

6 authors.

Adriani NikolakopoulouDepartment of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Anna ChaimaniDepartment of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Areti Angeliki VeronikiDepartment of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Haris S VasiliadisDepartment of Orthopaedics, University of Ioannina School of Medicine, Ioannina, Greece ; Molecular Cell Biology and Regenerative Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Christopher H SchmidCenter for Evidence Based Medicine and Department of Biostatistics, Program in Public Health, Brown University, Providence, Rhode Island, United States of America.
Georgia SalantiDepartment of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.

Funding

Modernizing Meta-Analysis to Facilitate Comparative Effectiveness ReviewsR01HS018574 · AHRQ · TUFTS MEDICAL CENTER · PI SCHMID, CHRISTOPHER H. · 2009 to 2011
$1.2M
AHRQ HHS R01 HS018574AHRQ HHS R01HS018574European Research Council 260559
6 · The paper itself

Abstract

Systematic reviews that employ network meta-analysis are undertaken and published with increasing frequency while related statistical methodology is evolving. Future statistical developments and evaluation of the existing methodologies could be motivated by the characteristics of the networks of interventions published so far in order to tackle real rather than theoretical problems. Based on the recently formed network meta-analysis literature we aim to provide an insight into the characteristics of networks in healthcare research. We searched PubMed until end of 2012 for meta-analyses that used any form of indirect comparison. We collected data from networks that compared at least four treatments regarding their structural characteristics as well as characteristics of their analysis. We then conducted a descriptive analysis of the various network characteristics. We included 186 networks of which 35 (19%) were star-shaped (treatments were compared to a common comparator but not between themselves). The median number of studies per network was 21 and the median number of treatments compared was 6. The majority (85%) of the non-star shaped networks included at least one multi-arm study. Synthesis of data was primarily done via network meta-analysis fitted within a Bayesian framework (113 (61%) networks). We were unable to identify the exact method used to perform indirect comparison in a sizeable number of networks (18 (9%)). In 32% of the networks the investigators employed appropriate statistical methods to evaluate the consistency assumption; this percentage is larger among recently published articles. Our descriptive analysis provides useful information about the characteristics of networks of interventions published the last 16 years and the methods for their analysis. Although the validity of network meta-analysis results highly depends on some basic assumptions, most authors did not report and evaluate them adequately. Reviewers and editors need to be aware of these assumptions and insist on their reporting and accuracy.

Indexed as

Meta-Analysis as TopicBayes TheoremBiomedical ResearchOdds RatioResearch Design

Identifiers

PMID24466222
PMCPMC3899297

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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 OpenQuestion graph.