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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
  • W. Ray Kim

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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  • 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, September.
    • Handle: RePEc:wly:hlthec:v:11:y:2002:i:6:p:551-566
    DOI: 10.1002/hec.739
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    References listed on IDEAS

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    Cited by:

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    2. A. E. Ades & A. J. Sutton, 2006. "Multiparameter evidence synthesis in epidemiology and medical decision‐making: current approaches," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 5-35, January.
    3. Mohamed El Alili & Johanna M. van Dongen & Jonas L. Esser & Martijn W. Heymans & Maurits W. van Tulder & Judith E. Bosmans, 2022. "A scoping review of statistical methods for trial‐based economic evaluations: The current state of play," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2680-2699, December.
    4. David J. Vanness & John Mullahy, 2012. "Moving Beyond Mean-based Evaluation of Health Care," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 52, Edward Elgar Publishing.
    5. Miguel A. Negrín & Francisco J. Vázquez-Polo & María Martel & Elías Moreno & Francisco J. Girón, 2010. "Bayesian Variable Selection in Cost-Effectiveness Analysis," IJERPH, MDPI, vol. 7(4), pages 1-20, April.

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