Evidence map›Paper›PMID 32028897›Full record

ReviewBMC medical research methodology2020

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis.

Michael Kossmeier, Ulrich S Tran, Martin Voracek

Open access · goldAbstract readReview
In one paragraph

Review in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 6 pooled it
8.0field-weighted citation impact, top 2% of its field
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

21 citing papers in PubMed, 6 syntheses or guidelines pooled it, 77 citations in OpenAlex.

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  6. Guideline
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  8. Visualization of Multi-indication Randomized Control Trial Evidence to Support Decision Making in Oncology: A Case Study on Bevacizumab.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
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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

3 authors at 1 institution in 2 countries.

Michael KossmeierDepartment of Basic Psychological Research and Research Methods, School of Psychology, University of Vienna, Liebiggasse 5, A-1010, Vienna, Austria. michael.kossmeier@univie.ac.at.ORCID 0000-0002-1246-0675
Ulrich S TranDepartment of Basic Psychological Research and Research Methods, School of Psychology, University of Vienna, Liebiggasse 5, A-1010, Vienna, Austria.ORCID 0000-0002-6589-3167
Martin VoracekDepartment of Basic Psychological Research and Research Methods, School of Psychology, University of Vienna, Liebiggasse 5, A-1010, Vienna, Austria. martin.voracek@univie.ac.at.ORCID 0000-0001-6109-6155
University of Vienna · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable.

methodsWe applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes.

resultsWe ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features.

conclusionsThe rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

Indexed as

Computer GraphicsMeta-Analysis as TopicResearch DesignHumansInformation Storage and RetrievalPublicationsReproducibility of ResultsSystematic Reviews as TopicData visualizationForest plotFunnel plotGalbraith plotGraphical displayL’Abbé plotMeta-analysisNetwork meta-analysisResearch synthesisSystematic reviews

Identifiers

PMID32028897
PMCPMC7006175
OpenAlexW3012641578

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.