Vitruvian plot: a visualisation tool for multiple outcomes in network meta-analysis

Ostinelli, Edoardo Giuseppe, Efthimiou, Orestis, Naci, Huseyin ORCID: 0000-0002-7192-5751, Furukawa, Toshi A., Leucht, Stefan, Salanti, Georgia, Wainwright, Laurence, Zangani, Caroline, De Crescenzo, Franco, Smith, Katharine, Stevens, Katherine, Liu, Qiang and Cipriani, Andrea (2022) Vitruvian plot: a visualisation tool for multiple outcomes in network meta-analysis. Evidence-Based Mental Health, 25 (e1). e65 - e70. ISSN 1362-0347

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Abstract

Objective: A network meta-analysis (NMA) usually assesses multiple outcomes across several treatment comparisons. The Vitruvian plot aims to facilitate communication of multiple outcomes from NMAs to patients and clinicians. Methods: We developed this tool following the recommendations on the communication of benefit-risk information from the available literature. We collected and implemented feedback from researchers, statisticians, methodologists, clinicians and people with lived experience of physical and mental health issues. Results: We present the Vitruvian plot, which graphically presents absolute estimates and relative performance of competing interventions against a common comparator for several outcomes of interest. We use two alternative colour schemes to highlight either the strength of statistical evidence or the confidence in the evidence. Confidence in the evidence is evaluated across six domains (within-study bias, reporting bias, indirectness, imprecision, heterogeneity and incoherence) using the Confidence in Network Meta-Analysis (CINeMA) system. Conclusions: The Vitruvian plot allows reporting of multiple outcomes from NMAs, with colourings appropriate to inform credibility of the presented evidence.

Item Type:	Article
Official URL:	https://ebmh.bmj.com/
Additional Information:	© 2022 The Authors
Divisions:	Health Policy
Subjects:	R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Date Deposited:	17 Jun 2022 13:18
Last Modified:	16 Nov 2024 19:27
URI:	http://eprints.lse.ac.uk/id/eprint/115393

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