PHP134 - Parameter uncertainty in value based multi criteria decision analysis: a systematic review of methods

Objectives: Multi criteria decision analysis (MCDA) aims to support decision-making where decisions are based on multiple criteria. In disciplines like engineering and environmental policy, MCDA is widely accepted and routinely used. The use of MCDA in HTA priority setting and reimbursement decisions is growing, but mostly limited to research projects. A factor that might influence acceptance is a perceived difficulty to value an MCDA’s outcome when its inputs and outputs contain uncertainties. When this is the case, decision makers might not feel confident in accepting or rejecting its outcome. The objective of this study is to systematically review how parameter uncertainty is taken into account in value-based MCDA methods in general, and to discuss which of the approaches is appropriate for the setting of reimbursement decision making in health care.

Methods: A systematic literature review was conducted using the Scopus database. Found abstracts were categorized by MCDA method used. Then, themes and families of methods were identified by two independent reviewers. Selected full text articles were read to identify methodological details.

Results: The search strategy identified 635 abstracts, mostly from engineering and environmental journals and only 1.6% in health journals. Identified themes were fuzzy set theory (n= 223), probabilistic framework (n= 68), deterministic sensitivity analysis (n= 140), Dempster-Shafer theory (n= 14), Bayesian framework (n= 7) and Grey theory (n= 8). A large number of papers considered the Analytic Hierarchy Process in combination with fuzzy set theory (n= 155).

Conclusions: In the health literature there is little attention for parameter uncertainty. Methods to deal with parameter uncertainty in MCDA must strike a balance between comprehensibility and understandability. Several complex methods are developed for parameter uncertainty, but there seems to be a gap between the theory and the implementation of those methods. For simple applications, methods like deterministic sensitivity analysis are likely to be sufficient.