Evidence map›Paper›PMID 37951949›Full record

ArticleSystematic reviews2023

Estimating and visualising the trade-off between benefits and harms on multiple clinical outcomes in network meta-analysis.

Virginia Chiocchia, Toshi A Furukawa, Johannes Schneider-Thoma, Spyridon Siafis, Andrea Cipriani, Stefan Leucht, Georgia Salanti

Abstract readNetwork Meta-Analysis
In one paragraph

Article in Systematic reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Virginia ChiocchiaInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. virginia.chiocchia@ispm.unibe.ch.ORCID 0000-0002-6196-3308
Toshi A FurukawaDepartment of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.
Johannes Schneider-ThomaDepartment of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany.
Spyridon SiafisDepartment of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany.
Andrea CiprianiDepartment of Psychiatry, University of Oxford, Oxford, UK.
Stefan LeuchtDepartment of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany.
Georgia SalantiInstitute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 179158
6 · The paper itself

Abstract

backgroundThe relative treatment effects estimated from network meta-analysis can be employed to rank treatments from the most preferable to the least preferable option. These treatment hierarchies are typically based on ranking metrics calculated from a single outcome. Some approaches have been proposed in the literature to account for multiple outcomes and individual preferences, such as the coverage area inside a spie chart, that, however, does not account for a trade-off between efficacy and safety outcomes. We present the net-benefit standardised area within a spie chart, [Formula: see text] to explore the changes in treatment performance with different trade-offs between benefits and harms, according to a particular set of preferences.

methodsWe combine the standardised areas within spie charts for efficacy and safety/acceptability outcomes with a value λ specifying the trade-off between benefits and harms. We derive absolute probabilities and convert outcomes on a scale between 0 and 1 for inclusion in the spie chart.

resultsWe illustrate how the treatments in three published network meta-analyses perform as the trade-off λ varies. The decrease of the [Formula: see text] quantity appears more pronounced for some drugs, e.g. haloperidol. Changes in treatment performance seem more frequent when SUCRA is employed as outcome measures in the spie charts.

conclusions[Formula: see text] should not be interpreted as a ranking metric but it is a simple approach that could help identify which treatment is preferable when multiple outcomes are of interest and trading-off between benefits and harms is important.

Indexed as

HaloperidolOutcome Assessment, Health CareHumansHaloperidolBenefitsHarmsMultiple outcomesNetwork meta-analysisSpie chartsTrade-offTreatment performance

Identifiers

PMID37951949
PMCPMC10638812

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.