Theoretical foundations of MCDA

Abstract

Decision-makers in the healthcare sector face a global challenge of developing robust, evidence-based methods for making decisions about whether to fund, cover, or reimburse medical technologies. Allocating scarce resources across technologies is difficult because a range of criteria are relevant to a healthcare decision, including the effectiveness, cost-effectiveness, and budget impact of the technology; the incidence, prevalence, and severity of the disease; the affected population group; the availability of alternative technologies; and the quality of the available evidence. When comparing healthcare technologies, decision-makers often need to make trade-offs between these criteria. Multi-criteria decision analysis (MCDA) is a tool that helps decision-makers summarize complex value trade-offs in a way that is consistent and transparent. It is comprised of a set of techniques that bring about an ordering of alternative decisions from most to least preferred, where each technology is ranked based on the extent to which it creates value through achieving a set of policy objectives. The purpose of this chapter was to provide a brief overview of the theoretical foundations of MCDA. We reviewed theories related to problem structuring and model building. We found problem structuring aimed to qualitatively determine policy objectives and the relevant criteria of value that affect decision-making. Model building theories sought to construct consistent representations of decision-makers' preferences and value trade-offs through value measurement models (multi-attribute value theory, multi-attribute utility theory, and the analytical hierarchy process), outranking (ELECTRE), and reference (weighted and lexicographic goal programming) models. We conclude that MCDA theory has largely been developed in other fields, and there is a need to develop MCDA theory that is adapted to the healthcare context.