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A Bayesian approach to economic analyses of clinical trials: the case of stenting versus balloon angioplasty

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Author Info

  • Maiwenn J. Al

    (Institute for Medical Technology Assessment, Erasmus University Rotterdam, Netherlands)

  • Ben A. Van Hout

    (Institute for Medical Technology Assessment, Erasmus University Rotterdam, Netherlands)

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    Abstract

    New results about the costs and effects of a new therapy may be weighted with prior information. As such, classical confidence intervals surrounding the costs and effects of a therapy may not reflect the real uncertainties. Bayesian techniques may improve this by formalizing the way that prior information is taken into account in assessing the new evidence. Costs and effects can be analysed separately, but also, when considering the balance between costs and effects, they can be analysed simultaneously. Here, an example is given using data from two trials that compared costs and effectiveness of stent implantation with balloon angioplasty. The Bayesian results make it clear that different prior distributions may lead to different decisions, and it is concluded that even Bayesian analysis may not always reflect the process of capturing the remaining uncertainties. Copyright © 2000 John Wiley & Sons, Ltd.

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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

    Volume (Year): 9 (2000)
    Issue (Month): 7 ()
    Pages: 599-609

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    Handle: RePEc:wly:hlthec:v:9:y:2000:i:7:p:599-609

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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    References

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    1. Daniel F. Heitjan & Alan J. Moskowitz & William Whang, 1999. "Bayesian estimation of cost-effectiveness ratios from clinical trials," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 191-201.
    2. 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.
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    Cited by:
    1. 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.
    2. Caterina Conigliani & Andrea Tancredi, 2009. "A Bayesian model averaging approach for cost-effectiveness analyses," Health Economics, John Wiley & Sons, Ltd., vol. 18(7), pages 807-821.
    3. Moreno, E. & Girón, F.J. & Martínez, M.L. & Vázquez-Polo, F.J. & Negrín, M.A., 2013. "Optimal treatments in cost-effectiveness analysis in the presence of covariates: Improving patient subgroup definition," European Journal of Operational Research, Elsevier, vol. 226(1), pages 173-182.
    4. Hoch, Jeffrey S. & Blume, Jeffrey D., 2008. "Measuring and illustrating statistical evidence in a cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 27(2), pages 476-495, March.
    5. David J. Vanness & W. Ray Kim, 2002. "Bayesian estimation, simulation and uncertainty analysis: the cost-effectiveness of ganciclovir prophylaxis in liver transplantation," Health Economics, John Wiley & Sons, Ltd., vol. 11(6), pages 551-566.
    6. 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.
    7. Daniel F. Heitjan & Huiling Li, 2004. "Bayesian estimation of cost-effectiveness: an importance-sampling approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(2), pages 191-198.
    8. F. J. Vázquez-Polo & M. A. Negr�n Hernández & B. González López-Valcárcel, 2005. "Using covariates to reduce uncertainty in the economic evaluation of clinical trial data," Health Economics, John Wiley & Sons, Ltd., vol. 14(6), pages 545-557.

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