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Cost‐effectiveness acceptability curves – caveats quantified

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  • Michał Jakubczyk
  • Bogumił Kamiński

Abstract

Cost‐effectiveness acceptability curves (CEACs) have become widely used in applied health technology assessment and at the same time are criticized as unreliable decision‐making tool. In this paper we show how using CEACs differs from maximizing expected net benefit (NB) and when it can lead to inconsistent decisions. In the case of comparing two alternatives we show the limits of the discrepancy between CEAC and expected NB approach and link it with expected value of perfect information. We also show how the shape of CEAC is influenced by the skewness of estimate of expected NB distribution, the correlation between cost and effect estimates and their variance. In the case of more than two options we show when using CEACs can lead to non‐transitive choices in pair‐wise comparisons and when it lacks independence of irrelevant alternatives property in joint comparisons. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Michał Jakubczyk & Bogumił Kamiński, 2010. "Cost‐effectiveness acceptability curves – caveats quantified," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 955-963, August.
  • Handle: RePEc:wly:hlthec:v:19:y:2010:i:8:p:955-963
    DOI: 10.1002/hec.1534
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    File URL: https://doi.org/10.1002/hec.1534
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    References listed on IDEAS

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    1. Eugene M. Laska & Morris Meisner & Carole Siegel & Aaron A. Stinnett, 1999. "Ratio‐based and net benefit‐based approaches to health care resource allocation: proofs of optimality and equivalence," Health Economics, John Wiley & Sons, Ltd., vol. 8(2), pages 171-174, March.
    2. Hadar, Josef & Russell, William R, 1969. "Rules for Ordering Uncertain Prospects," American Economic Review, American Economic Association, vol. 59(1), pages 25-34, March.
    3. Andrew Briggs & Paul Fenn, 1998. "Confidence intervals or surfaces? Uncertainty on the cost-effectiveness plane," Health Economics, John Wiley & Sons, Ltd., vol. 7(8), pages 723-740.
    4. Peter J. Hammond, 1976. "Changing Tastes and Coherent Dynamic Choice," Review of Economic Studies, Oxford University Press, vol. 43(1), pages 159-173.
    5. Anthony O'Hagan & John W. Stevens, 2003. "Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality?," Health Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 33-49, January.
    6. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261, May.
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    Cited by:

    1. Michał Jakubczyk & Bogumił Kamiński, 2017. "Fuzzy approach to decision analysis with multiple criteria and uncertainty in health technology assessment," Annals of Operations Research, Springer, vol. 251(1), pages 301-324, April.
    2. Klemen Naveršnik, 2015. "Output correlations in probabilistic models with multiple alternatives," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(2), pages 133-139, March.
    3. Ernst, Richard, 2017. "Theories of Health Care Cost-Effectiveness Analysis," SocArXiv gjbcp, Center for Open Science.
    4. Moreno, Elías & Girón, F.J. & Vázquez-Polo, F.J. & Negrín, M.A., 2012. "Optimal healthcare decisions: The importance of the covariates in cost–effectiveness analysis," European Journal of Operational Research, Elsevier, vol. 218(2), pages 512-522.
    5. Dirk Müller & Eleanor Pullenayegum & Afschin Gandjour, 2015. "Impact of small study bias on cost-effectiveness acceptability curves and value of information analyses," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(2), pages 219-223, March.
    6. Maiwenn Al, 2013. "Cost-Effectiveness Acceptability Curves Revisited," PharmacoEconomics, Springer, vol. 31(2), pages 93-100, February.

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