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Incorporating MCDA into HTA: challenges and potential solutions, with a focus on lower income settings

BACKGROUND: Multicriteria decision analysis (MCDA) has the potential to bring more structure and transparency to health technology assessment (HTA). The objective of this paper is to highlight key methodological and practical challenges facing the use of MCDA for HTA, with a particular focus on lowe...

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Detalles Bibliográficos
Autores principales: Marsh, Kevin, Thokala, Praveen, Youngkong, Sitaporn, Chalkidou, Kalipso
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6225551/
https://www.ncbi.nlm.nih.gov/pubmed/30455602
http://dx.doi.org/10.1186/s12962-018-0125-8
Descripción
Sumario:BACKGROUND: Multicriteria decision analysis (MCDA) has the potential to bring more structure and transparency to health technology assessment (HTA). The objective of this paper is to highlight key methodological and practical challenges facing the use of MCDA for HTA, with a particular focus on lower and middle-income countries (LMICs), and to highlight potential solutions to these challenges. METHODOLOGICAL CHALLENGES: Key lessons from existing applications of MCDA to HTA are summarized, including: that the socio-technical design of the MCDA reflect the local decision problem; the criteria set properties of additive models are understood and applied; and the alternative approaches for estimating opportunity cost, and the challenges with these approaches are understood. PRACTICAL CHALLENGES: Existing efforts to implement HTA in LMICs suggest a number of lessons that can help overcome the practical challenges facing the implementation of MCDA in LMICs, including: adapting inputs from other settings and from expert opinion; investing in technical capacity; embedding the MCDA in the decision-making process; and ensuring that the MCDA design reflects local cultural and social factors. CONCLUSION: MCDA has the potential to improve decision making in LMICs. For this potential to be achieved, it is important that the lessons from existing applications of MCDA are learned.