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Health Technology Assessment in the Cost-Disutility Plane

Author

Listed:
  • Simon Eckermann

    (Flinders Centre for Clinical Change and Health Care Research, Flinders University, Adelaide, Australia, simon.eckermann@flinders.edu.au)

  • Andrew Briggs
  • Andrew R. Willan

    (SickKids Research Institute and University of Toronto, Ontario, Canada)

Abstract

Previously, comparisons of multiple strategies in health technology assessment have been undertaken on the incremental cost-effectiveness plane using efficiency frontiers and cost-effectiveness acceptability curves. This article proposes shifting the comparison of multiple strategies to the cost-disutility plane. Evidence-based decision making requires comparison of all strategies against each other. Consequently, the origin in the incremental cost-effectiveness plane cannot be the appropriate reference point in comparing multiple nondominated strategies. A linear transformation onto the cost-disutility plane allows an equivalent comparison of net benefit and permits the use of standard efficiency measurement methods to estimate 1) the degree of dominance (technical inefficiency) of dominated strategies and 2) the net benefit inefficiency (i.e., losses in net benefit relative to an optimal strategy). In comparing strategies under uncertainty, a comparison of loss in net benefit leads to the expected net loss frontier, which, unlike cost effectiveness acceptability curves, directly identifies differences in expected net benefit (net loss) and the expected value of perfect information. Thus, decision makers can be better informed about the choice of optimal strategy and the potential value of future research to resolve uncertainty. Comparing strategies in the cost-disutility plane is suggested to better inform decision making and to provide a link between the cost-effectiveness literature and efficiency measurement methods.

Suggested Citation

  • Simon Eckermann & Andrew Briggs & Andrew R. Willan, 2008. "Health Technology Assessment in the Cost-Disutility Plane," Medical Decision Making, , vol. 28(2), pages 172-181, March.
    • Handle: RePEc:sae:medema:v:28:y:2008:i:2:p:172-181
    DOI: 10.1177/0272989X07312474
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    References listed on IDEAS

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    1. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    2. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
    3. Aaron A. Stinnett & A. David Paltiel, 1997. "Estimating CE Ratios under Second-order Uncertainty," Medical Decision Making, , vol. 17(4), pages 483-489, October.
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    Cited by:

    1. Simon Eckermann & Andrew R. Willan, 2009. "Globally optimal trial design for local decision making," Health Economics, John Wiley & Sons, Ltd., vol. 18(2), pages 203-216, February.
    2. Simon Eckermann & Andrew Willan, 2011. "Presenting Evidence and Summary Measures to Best Inform Societal Decisions When Comparing Multiple Strategies," PharmacoEconomics, Springer, vol. 29(7), pages 563-577, July.
    3. Eckermann, Simon & Coelli, Tim, 2013. "Including quality attributes in efficiency measures consistent with net benefit: Creating incentives for evidence based medicine in practice," Social Science & Medicine, Elsevier, vol. 76(C), pages 159-168.
    4. Nikki McCaffrey & Meera Agar & Janeane Harlum & Jonathon Karnon & David Currow & Simon Eckermann, 2015. "Better Informing Decision Making with Multiple Outcomes Cost-Effectiveness Analysis under Uncertainty in Cost-Disutility Space," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-19, March.

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