Prioritizing Healthcare Interventions: A Comparison of Multicriteria Decision Analysis and Cost-Effectiveness Analysis

Published:September 23, 2021DOI:


      • Multicriteria decision analysis (MCDA) and cost-effectiveness analysis (CEA) are commonly used methods to aggregate value preferences to inform economic evaluations in healthcare. The relative merits of these different methods are being increasingly debated, but little empirical evidence is available to provide direct evidence of their relative performance.
      • This study directly compares the application of MCDA and CEA methods to a common research question: the prioritization of nonsurgical management options for knee osteoarthritis. We identify systematic differences between the 2 approaches in the aggregation of value criteria, particularly on the criteria of cost and risks of harm, and several considerations for the design of MCDA studies: treatment of opportunity cost, framing effects, and value models used to aggregate preference criteria.
      • These findings highlight the need for careful consideration of the design and interpretation of MCDA models in healthcare decision making. We support recent recommendations that cost should not be considered as part of the value model in MCDA studies. The appropriateness of value models chosen to aggregate MCDA preference criteria should be carefully evaluated. CEA based on expected value may fail to capture the stakeholders’ preferences for risk aversion.



      To investigate the extent to which stated preferences for treatment criteria elicited using multicriteria decision analysis (MCDA) methods are consistent with the trade-offs (implicitly) applied in cost-effectiveness analysis (CEA), and the impact of any differences on the prioritization of treatments.


      We used existing MCDA and CEA models developed to evaluate interventions for knee osteoarthritis in the New Zealand population. We established equivalent input parameters for each model, for the criteria “treatment effectiveness,” “cost,” “risk of serious harms,” and “risk of mild-to-moderate harms” across a comprehensive range of (hypothetical) interventions to produce a complete ranking of interventions from each model. We evaluated the consistency of these rankings between the 2 models and investigated any systematic differences between the (implied) weight placed on each criterion in determining rankings.


      There was an overall moderate-to-strong correlation in intervention rankings between the MCDA and CEA models (Spearman correlation coefficient = 0.51). Nevertheless, there were systematic differences in the evaluation of trade-offs between intervention attributes and the resulting weights placed on each criterion. The CEA model placed lower weights on risks of harm and much greater weight on cost (at all accepted levels of willingness-to-pay per quality-adjusted life-year than did respondents to the MCDA survey.


      MCDA and CEA approaches to inform intervention prioritization may give systematically different results, even when considering the same criteria and input data. These differences should be considered when designing and interpreting such studies to inform treatment prioritization decisions.


      To read this article in full you will need to make a payment


      Subscribe to Value in Health
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Phelps C.E.
        • Lakdawalla D.N.
        • Basu A.
        • Drummond M.F.
        • Towse A.
        • Danzon P.M.
        Approaches to aggregation and decision making-a health economics approach: an ISPOR Special Task Force report [5].
        Value Health. 2018; 21: 146-154
        • Angelis A.
        • Kanavos P.
        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.
        Pharmacoeconomics. 2016; 34: 435-446
        • Garau M.
        • Devlin N.J.
        Using MCDA as a decision aid in health technology appraisal for coverage decisions: opportunities, challenges and unresolved questions.
        in: Marsh K. Goetghebeur M. Thokala P. Baltussen R. Multi-Criteria Decision Analysis to Support Healthcare Decisions. Springer, Cham, Switzerland2017: 277-298
        • Marsh K.
        • IJzerman M.
        • Thokala P.
        • et al.
        Multiple criteria decision analysis for health care decision making--emerging good research practices: report 2 of the ISPOR MCDA Emerging Good Research Practices Task Force.
        Value Health. 2016; 19: 125-137
        • Campillo-Artero C.
        • Puig-Junoy J.
        • Culyer A.J.
        Does MCDA trump CEA?.
        Appl Health Econ Health Policy. 2018; 16: 147-151
        • Keeney R.L.
        Common mistakes in making value trade-offs.
        Oper Res. 2002; 50: 935-945
        • Keeney R.L.
        • Gregory R.S.
        Selecting attributes to measure the achievement of objectives.
        Oper Res. 2005; 52: 1-11
        • Chua J.
        • Briggs A.M.
        • Hansen P.
        • Chapple C.
        • Abbott J.H.
        Choosing interventions for hip or knee osteoarthritis-what matters to stakeholders? A mixed methods study.
        Osteoarthr Cartil Open. 2020; 2100062
        • Chua J.
        • Hansen P.
        • Briggs A.M.
        • Wilson R.
        • Gwynne-Jones D.
        • Abbott J.H.
        Stakeholders’ preferences for osteoarthritis interventions in a health service: a cross-sectional study using multi-criteria decision analysis.
        Osteoarthr Cartil Open. 2020; 2100110
        • Wilson R.
        • Abbott J.H.
        Development and validation of a new population-based simulation model of osteoarthritis in New Zealand.
        Osteoarthr Cartil. 2018; 26: 531-539
        • Wilson R.
        • Chua J.
        • Briggs A.M.
        • Abbott J.H.
        The cost-effectiveness of recommended adjunctive interventions for knee osteoarthritis: results from a computer simulation model.
        Osteoarthr Cartil Open. 2020; 2100123
        • Hansen P.
        • Devlin N.
        Multi-criteria decision analysis (MCDA) in healthcare decision-making.
        Oxford University Press, Oxford, United Kingdom2019
        • Baltussen R.
        • Marsh K.
        • Thokala P.
        • et al.
        Multicriteria decision analysis to support health technology assessment agencies: benefits, limitations, and the way forward.
        Value Health. 2019; 22: 1283-1288
        • Marsh K.D.
        • Sculpher M.
        • Caro J.J.
        • Tervonen T.
        The use of MCDA in HTA: great potential, but more effort needed.
        Value Health. 2018; 21: 394-397
        • Hansen P.
        • Ombler F.
        A new method for scoring additive multi-attribute value models using pairwise rankings of alternatives.
        J Multi Criteria Decis Anal. 2008; 15: 87-107
      1. Guideline for the management of knee and hip osteoarthritis. Royal Australian College of General Practitioners.
        • Sachs J.D.
        Macroeconomics and health: Investing in health for economic development. Report of the Commission on Macroeconomics and Health.
        World Health Organization, Geneva, Switzerland2001
        • Golan O.
        • Hansen P.
        • Kaplan G.
        • Tal O.
        Health technology prioritization: which criteria for prioritizing new technologies and what are their relative weights?.
        Health Policy. 2011; 102: 126-135
        • Wilson R.
        • Abbott J.H.
        The projected burden of knee osteoarthritis in New Zealand: healthcare expenditure and total joint replacement provision.
        N Z Med J. 2019; 132: 53-65
        • Goetghebeur M.M.
        • Wagner M.
        • Khoury H.
        • Levitt R.J.
        • Erickson L.J.
        • Rindress D.
        Evidence and Value: impact on DEcisionMaking--the EVIDEM framework and potential applications.
        BMC Health Serv Res. 2008; 8: 270
        • Claxton K.
        • Martin S.
        • Soares M.
        • et al.
        Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold.
        Health Technol Assess. 2015; 19: 1-vi
        • Edney L.C.
        • Haji Ali Afzali H.
        • Cheng T.C.
        • Karnon J.
        Estimating the reference incremental cost-effectiveness ratio for the Australian health system.
        Pharmacoeconomics. 2018; 36: 239-252
        • Nitzsch R. von
        • Weber M.
        The effect of attribute ranges on weights in multiattribute utility measurements.
        Manag Sci. 1993; 39: 937-943
        • Fischer G.W.
        Range sensitivity of attribute weights in multiattribute value models.
        Organ Behav Hum Decis Processes. 1995; 62: 252-266
        • Peacock S.J.
        • Richardson J.R.J.
        • Carter R.
        • Edwards D.
        Priority setting in health care using multi-attribute utility theory and programme budgeting and marginal analysis (PBMA).
        Soc Sci Med. 2007; 64: 897-910