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Review
. 2015 May;33(5):445-55.
doi: 10.1007/s40273-014-0251-x.

A review and classification of approaches for dealing with uncertainty in multi-criteria decision analysis for healthcare decisions

Henk Broekhuizen  1 , Catharina G M Groothuis-Oudshoorn, Janine A van Til, J Marjan Hummel, Maarten J IJzerman
Affiliations
Review

A review and classification of approaches for dealing with uncertainty in multi-criteria decision analysis for healthcare decisions

Henk Broekhuizen et al. Pharmacoeconomics. 2015 May.

Abstract

Multi-criteria decision analysis (MCDA) is increasingly used to support decisions in healthcare involving multiple and conflicting criteria. Although uncertainty is usually carefully addressed in health economic evaluations, whether and how the different sources of uncertainty are dealt with and with what methods in MCDA is less known. The objective of this study is to review how uncertainty can be explicitly taken into account in MCDA and to discuss which approach may be appropriate for healthcare decision makers. A literature review was conducted in the Scopus and PubMed databases. Two reviewers independently categorized studies according to research areas, the type of MCDA used, and the approach used to quantify uncertainty. Selected full text articles were read for methodological details. The search strategy identified 569 studies. The five approaches most identified were fuzzy set theory (45% of studies), probabilistic sensitivity analysis (15%), deterministic sensitivity analysis (31%), Bayesian framework (6%), and grey theory (3%). A large number of papers considered the analytic hierarchy process in combination with fuzzy set theory (31%). Only 3% of studies were published in healthcare-related journals. In conclusion, our review identified five different approaches to take uncertainty into account in MCDA. The deterministic approach is most likely sufficient for most healthcare policy decisions because of its low complexity and straightforward implementation. However, more complex approaches may be needed when multiple sources of uncertainty must be considered simultaneously.

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Figures

Fig. 1
Fig. 1
Distribution of identified studies by their year of publication and divided into the approach in which they were categorized
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