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Bayesian estimation, simulation and uncertainty analysis: the cost-effectiveness of ganciclovir prophylaxis in liver transplantation

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  • David J. Vanness

    (Division of Health Care Policy & Research, Mayo Clinic, Rochester, Minnesota, USA)

  • W. Ray Kim

    (Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA)

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    Abstract

    This paper demonstrates the usefulness of combining simulation with Bayesian estimation methods in analysis of cost-effectiveness data collected alongside a clinical trial. Specifically, we use Markov Chain Monte Carlo (MCMC) to estimate a system of generalized linear models relating costs and outcomes to a disease process affected by treatment under alternative therapies. The MCMC draws are used as parameters in simulations which yield inference about the relative cost-effectiveness of the novel therapy under a variety of scenarios. Total parametric uncertainty is assessed directly by examining the joint distribution of simulated average incremental cost and effectiveness. The approach allows flexibility in assessing treatment in various counterfactual premises and quantifies the global effect of parametric uncertainty on a decision-maker's confidence in adopting one therapy over the other. Copyright © 2002 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/hec.739
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    Bibliographic Info

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

    Volume (Year): 11 (2002)
    Issue (Month): 6 ()
    Pages: 551-566

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    Handle: RePEc:wly:hlthec:v:11:y:2002:i:6:p:551-566

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

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    1. Harsanyi, John C., 1994. "Games with Incomplete Information," Nobel Prize in Economics documents 1994-1, Nobel Prize Committee.
    2. Andrew H. Briggs & David E. Wonderling & Christopher Z. Mooney, 1997. "Pulling cost-effectiveness analysis up by its bootstraps: A non-parametric approach to confidence interval estimation," Health Economics, John Wiley & Sons, Ltd., vol. 6(4), pages 327-340.
    3. Cameron, A Colin & Trivedi, Pravin K, 1986. "Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(1), pages 29-53, January.
    4. Joanne Lord & Maxwell A. Asante, 1999. "Estimating uncertainty ranges for costs by the bootstrap procedure combined with probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(4), pages 323-333.
    5. Geweke, John, 2001. "Bayesian econometrics and forecasting," Journal of Econometrics, Elsevier, vol. 100(1), pages 11-15, January.
    6. 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.
    7. Maiwenn J. Al & Ben A. Van Hout, 2000. "A Bayesian approach to economic analyses of clinical trials: the case of stenting versus balloon angioplasty," Health Economics, John Wiley & Sons, Ltd., vol. 9(7), pages 599-609.
    8. Andrew H. Briggs, 2000. "Handling Uncertainty in Cost-Effectiveness Models," PharmacoEconomics, Springer Healthcare | Adis, vol. 17(5), pages 479-500.
    9. Anthony O'Hagan & John W. Stevens, 2001. "A framework for cost-effectiveness analysis from clinical trial data," Health Economics, John Wiley & Sons, Ltd., vol. 10(4), pages 303-315.
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