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A simple way to unify multicriteria decision analysis (MCDA) and stochastic multicriteria acceptability analysis (SMAA) using a Dirichlet distribution in benefit–risk assessment
Author(s) -
SaintHilary Gaelle,
Cadour Stephanie,
Robert Veronique,
Gasparini Mauro
Publication year - 2017
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201600113
Subject(s) - multiple criteria decision analysis , probabilistic logic , decision analysis , computer science , generalization , risk analysis (engineering) , distribution (mathematics) , ranking (information retrieval) , mathematics , operations research , mathematical optimization , statistics , machine learning , artificial intelligence , medicine , mathematical analysis
Quantitative methodologies have been proposed to support decision making in drug development and monitoring. In particular, multicriteria decision analysis (MCDA) and stochastic multicriteria acceptability analysis (SMAA) are useful tools to assess the benefit–risk ratio of medicines according to the performances of the treatments on several criteria, accounting for the preferences of the decision makers regarding the relative importance of these criteria. However, even in its probabilistic form, MCDA requires the exact elicitations of the weights of the criteria by the decision makers, which may be difficult to achieve in practice. SMAA allows for more flexibility and can be used with unknown or partially known preferences, but it is less popular due to its increased complexity and the high degree of uncertainty in its results. In this paper, we propose a simple model as a generalization of MCDA and SMAA, by applying a Dirichlet distribution to the weights of the criteria and by making its parameters vary. This unique model permits to fit both MCDA and SMAA, and allows for a more extended exploration of the benefit–risk assessment of treatments. The precision of its results depends on the precision parameter of the Dirichlet distribution, which could be naturally interpreted as the strength of confidence of the decision makers in their elicitation of preferences.