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

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 p...

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Detalles Bibliográficos
Autores principales: Chiocchia, Virginia, Furukawa, Toshi A., Schneider-Thoma, Johannes, Siafis, Spyridon, Cipriani, Andrea, Leucht, Stefan, Salanti, Georgia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638812/
https://www.ncbi.nlm.nih.gov/pubmed/37951949
http://dx.doi.org/10.1186/s13643-023-02376-1
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-023-02376-1.