Value-Based Assessment of New Medical Technologies: Towards a Robust Methodological Framework for the Application of Multiple Criteria Decision Analysis in the Context of Health Technology Assessment

Aris Angelis () and Panos Kanavos
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Aris Angelis: LSE Health, London School of Economics and Political Science
Panos Kanavos: LSE Health, London School of Economics and Political Science

PharmacoEconomics, 2016, vol. 34, issue 5, No 3, 435-446

Abstract: Abstract In recent years, multiple criteria decision analysis (MCDA) has emerged as a likely alternative to address shortcomings in health technology assessment (HTA) by offering a more holistic perspective to value assessment and acting as an alternative priority setting tool. In this paper, we argue that MCDA needs to subscribe to robust methodological processes related to the selection of objectives, criteria and attributes in order to be meaningful in the context of healthcare decision making and fulfil its role in value-based assessment (VBA). We propose a methodological process, based on multi-attribute value theory (MAVT) methods comprising five distinct phases, outline the stages involved in each phase and discuss their relevance in the HTA process. Importantly, criteria and attributes need to satisfy a set of desired properties, otherwise the outcome of the analysis can produce spurious results and misleading recommendations. Assuming the methodological process we propose is adhered to, the application of MCDA presents three very distinct advantages to decision makers in the context of HTA and VBA: first, it acts as an instrument for eliciting preferences on the performance of alternative options across a wider set of explicit criteria, leading to a more complete assessment of value; second, it allows the elicitation of preferences across the criteria themselves to reflect differences in their relative importance; and, third, the entire process of preference elicitation can be informed by direct stakeholder engagement, and can therefore reflect their own preferences. All features are fully transparent and facilitate decision making.

Date: 2016
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DOI: 10.1007/s40273-015-0370-z

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