Evidence map›Paper›PMID 27926924›Full record

ArticlePloS one2016

The Impact of Excluding Trials from Network Meta-Analyses - An Empirical Study.

Jing Zhang, Yiping Yuan, Haitao Chu

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

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

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Multifaceted Music Therapy for Depression in Dementia: A Network Meta-Analysis of Randomized Controlled Trials.European journal of investigation in health, psychology and education · 2024
    Article
  11. Article
  12. 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. Bayesian Network Meta-analysis of Multiple Outcomes in Dental Research.The journal of evidence-based dental practice · 2020
    Article
  15. Article
  16. Article
  17. Article
  18. Article
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

3 authors.

Jing ZhangDepartment of Epidemiology and Biostatistics, University of Maryland School of Public Health College Park, Maryland, United States of America.
Yiping YuanSchool of Statistics, University of Minnesota Minneapolis, Minnesota, United States of America.
Haitao ChuDivision of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota, United States of America.

Funding

Women's CancerP30CA077598 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI Douglas Yee · 1998 to 2026
$100.4M
Research CoreU54MD008620 · NIMHD · UNIVERSITY OF MINNESOTA · PI DURANT, RAEGAN WINSTON, SHIKANY, JAMES M · 2013 to 2017
$13.5M
Prevention of Lower Urinary Tract Symptoms in Women: Bladder Health Scientific and Data Coordinating Center (PLUS-SDCC) (U01)U01DK106786 · NIDDK · UNIVERSITY OF MINNESOTA · PI HARLOW, BERNARD L, RUDSER, KYLE · 2015 to 2019
$8.4M
Statistical Methods and Software for Multivariate Meta-analysisR21LM012197 · NLM · UNIVERSITY OF MINNESOTA · PI CHU, HAITAO · 2015 to 2016
$385k
Bayesian Methods and Software for Patient-Centered Network Meta-Analysis of BinarR21AI103012 · NIAID · UNIVERSITY OF MINNESOTA · PI CHU, HAITAO · 2013 to 2014
$385k
Aiding Effective Decision Making in Dental Research Using Network Meta-analysisR03DE024750 · NIDCR · UNIVERSITY OF MINNESOTA · PI CHU, HAITAO, JOHN, MIKE · 2015 to 2016
$292k
NCI NIH HHS P30 CA077598NIAID NIH HHS R21 AI103012NIDCR NIH HHS R03 DE024750NIDDK NIH HHS U01 DK106786NIMHD NIH HHS U54 MD008620NLM NIH HHS R21 LM012197
6 · The paper itself

Abstract

Network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, which has an inherent appeal for clinicians, patients, and policy decision makers. Two recent reports have shown that the impact of excluding a treatment on NMAs can be substantial. However, no one has assessed the impact of excluding a trial from NMAs, which is important because many NMAs selectively include trials in the analysis. This article empirically examines the impact of trial exclusion using both the arm-based (AB) and contrast-based (CB) approaches, by reanalyzing 20 published NMAs involving 725 randomized controlled trials and 449,325 patients. For the population-averaged absolute risk estimates using the AB approach, the average fold changes across all networks ranged from 1.004 (with standard deviation 0.004) to 1.072 (with standard deviation 0.184); while the maximal fold changes ranged from 1.032 to 2.349. In 12 out of 20 NMAs, a 1.20-fold or larger change is observed in at least one of the population-averaged absolute risk estimates. In addition, while excluding a trial can substantially change the estimated relative effects (e.g., log odds ratios), there is no systematic difference in terms of changes between the two approaches. Changes in treatment rankings are observed in 7 networks and changes in inconsistency are observed in 3 networks. We do not observe correlations between changes in treatment effects, treatment rankings and inconsistency. Finally, we recommend rigorous inclusion and exclusion criteria, logical study selection process, and reasonable network geometry to ensure robustness and generalizability of the results of NMAs.

Indexed as

Empirical ResearchAdministrative PersonnelDecision MakingHumansNetwork Meta-Analysis as TopicOdds RatioRandomized Controlled Trials as Topic

Identifiers

PMID27926924
PMCPMC5142775

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.