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

Journal article

BACKGROUND The 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. METHODS We 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. RESULTS We 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

Authors: Chiocchia, V; Furukawa, TA; Schneider-Thoma, J; Siafis, S; Cipriani, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: Systematic Reviews
• Volume: 12
• Issue: 1
• Pages: 209-209
• Article number: 209
• Publication date: 2023-11-11
• DOI: 10.1186/s13643-023-02376-1
• EISSN: 2046-4053
• ISSN: 2046-4053
• Language: English
• Keywords: Medicine; Meta-analysis; Confidence interval; Machine learning; Mathematics; Chart; Computer science; Internal medicine; Data mining; Ranking (information retrieval); Outcome (game theory); Algorithm; Statistics