Considering multiple outcomes with different weights informed the hierarchy of interventions in network meta-analysis

Ranking metrics in network meta-analysis (NMA) are computed separately for each outcome. Our aim is to 1) present graphical ways to group competing interventions considering multiple outcomes and 2) use conjoint analysis for placing weights on the various outcomes based on the stakeholders’ preferen...

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Bibliographic Details
Published in:	Journal of clinical epidemiology 2023-02, Vol.154, p.188-196
Main Authors:	Mavridis, Dimitris, Nikolakopoulou, Adriani, Moustaki, Irini, Chaimani, Anna, Porcher, Raphaël, Boutron, Isabelle, Ravaud, Philippe
Format:	Article
Language:	eng
Subjects:	Antipsychotics Clinical trials Clustering Conjoint analysis Epidemiology Estimates Humans Intervention Life Sciences Mental disorders Meta-analysis Methodology Multidimensional scaling Network Meta-Analysis Patients Preferences Psychosis Psychotic Disorders Psychotropic drugs Ranking Santé publique et épidémiologie Schizophrenia Stakeholders Statistics Weighting
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Summary:	Ranking metrics in network meta-analysis (NMA) are computed separately for each outcome. Our aim is to 1) present graphical ways to group competing interventions considering multiple outcomes and 2) use conjoint analysis for placing weights on the various outcomes based on the stakeholders’ preferences. We used multidimensional scaling (MDS) and hierarchical tree clustering to visualize the extent of similarity of interventions in terms of the relative effects they produce through a random effect NMA. We reanalyzed a published network of 212 psychosis trials taking three outcomes into account as follows: reduction in symptoms of schizophrenia, all-cause treatment discontinuation, and weight gain. Conjoint analysis provides a mathematical method to transform judgements into weights that can be subsequently used to visually represent interventions on a two-dimensional plane or through a dendrogram. These plots provide insightful information about the clustering of interventions. Grouping interventions can help decision makers not only to identify the optimal ones in terms of benefit-risk balance but also choose one from the best cluster based on other grounds, such as cost, implementation etc. Placing weights on outcomes allows considering patient profile or preferences.
ISSN:	0895-4356 1878-5921