Evidence map›Paper›PMID 32524754›Full record

ArticleResearch synthesis methods2021

The Kilim plot: A tool for visualizing network meta-analysis results for multiple outcomes.

Michael Seo, Toshi A Furukawa, Areti Angeliki Veroniki, Toby Pillinger, Anneka Tomlinson, Georgia Salanti, Andrea Cipriani, Orestis Efthimiou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research synthesis methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 9 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

27 citing papers in PubMed, 9 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

8 authors.

Michael SeoInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.ORCID https://orcid.org/0000-0002-5229-590X
Toshi A FurukawaDepartments of Health Promotion and Human Behavior and of Clinical Epidemiology, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.ORCID https://orcid.org/0000-0003-2159-3776
Areti Angeliki VeronikiDepartment of Primary Education, School of Education, University of Ioannina, Greece.ORCID https://orcid.org/0000-0001-6388-4825
Toby PillingerInstitute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.ORCID https://orcid.org/0000-0002-6074-2626
Anneka TomlinsonDepartment of Psychiatry, University of Oxford, Oxford, UK.
Georgia SalantiInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.ORCID https://orcid.org/0000-0002-3830-8508
Andrea CiprianiDepartment of Psychiatry, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0001-5179-8321
Orestis EfthimiouInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.ORCID https://orcid.org/0000-0002-0955-7572

Funding

Department of Health RP-2017-08-ST2-006European Union's Horizon 2020 754936Public Health Research Programme BRC-1215-20005Public Health Research Programme RP-2017-08-ST2-006Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 179158Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 180083
6 · The paper itself

Abstract

Network meta-analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes is of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every-day clinical practice, such as the odds ratios. In this article, we aim to facilitate the clinical decision-making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Our plot compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatment effects, while it illustrates absolute, rather than relative, effects of interventions. Moreover, it can be easily modified to include considerations regarding clinically important effects. To showcase our method, we use data from a network of studies in antidepressants. All analyses are performed in R and we provide the source code needed to produce the Kilim plot, as well as an interactive web application.

Indexed as

Data VisualizationAntidepressive AgentsComputer GraphicsDecision MakingDepressionHumansNetwork Meta-Analysis as TopicOutcome Assessment, Health CareResearch DesignSoftwareAntidepressive Agentsindirect comparisonsmixed evidencemultiple outcomesmultiple treatments meta-analysisvisualization

Identifiers

PMID32524754
PMCPMC7818463

What OpenQuestion holds

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LicenceCC BY
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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.