Evidence map›Paper›PMID 32509807›Full record

ArticleFrontiers in veterinary science2020

How to Conduct a Bayesian Network Meta-Analysis.

Dapeng Hu, Annette M O'Connor, Chong Wang, Jan M Sargeant, Charlotte B Winder

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in veterinary science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 25 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 25 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

41 citing papers in PubMed, 25 syntheses or guidelines pooled it.

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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

5 authors.

Dapeng HuDepartment of Statistics, Iowa State University, Ames, IA, United States.
Annette M O'ConnorDepartment of Large Animal Clinical Sciences, College of Veterinary Medicine, Michigan State University, East Lansing, MI, United States.
Chong WangDepartment of Statistics, Iowa State University, Ames, IA, United States.
Jan M SargeantDepartment of Population Medicine, University of Guelph, Guelph, ON, Canada.
Charlotte B WinderDepartment of Population Medicine, University of Guelph, Guelph, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Network meta-analysis is a general approach to integrate the results of multiple studies in which multiple treatments are compared, often in a pairwise manner. In this tutorial, we illustrate the procedures for conducting a network meta-analysis for binary outcomes data in the Bayesian framework using example data. Our goal is to describe the workflow of such an analysis and to explain how to generate informative results such as ranking plots and treatment risk posterior distribution plots. The R code used to conduct a network meta-analysis in the Bayesian setting is provided at GitHub.

Indexed as

Bayesiannetwork meta-analysissystematic reviewtutorialveterinary science

Identifiers

PMID32509807
PMCPMC7248597

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.