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A Bayesian approach to Markov modelling in cost‐effectiveness analyses: application to taxane use in advanced breast cancer

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  • Nicola J. Cooper
  • Keith R. Abrams
  • Alex J. Sutton
  • David Turner
  • Paul C. Lambert

Abstract

Summary. The paper demonstrates how cost‐effectiveness decision analysis may be implemented from a Bayesian perspective, using Markov chain Monte Carlo simulation methods for both the synthesis of relevant evidence input into the model and the evaluation of the model itself. The desirable aspects of a Bayesian approach for this type of analysis include the incorporation of full parameter uncertainty, the ability to perform all the analysis, including each meta‐analysis, in a single coherent model and the incorporation of expert opinion either directly or regarding the relative credibility of different data sources. The method is described, and its ease of implementation demonstrated, through a practical example to evaluate the cost‐effectiveness of using taxanes for the second‐line treatment of advanced breast cancer compared with conventional treatment. For completeness, the results from the Markov chain Monte Carlo simulation model are compared and contrasted with those from a classical Monte Carlo simulation model.

Suggested Citation

  • Nicola J. Cooper & Keith R. Abrams & Alex J. Sutton & David Turner & Paul C. Lambert, 2003. "A Bayesian approach to Markov modelling in cost‐effectiveness analyses: application to taxane use in advanced breast cancer," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(3), pages 389-405, October.
    • Handle: RePEc:bla:jorssa:v:166:y:2003:i:3:p:389-405
    DOI: 10.1111/1467-985X.00283
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    References listed on IDEAS

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    1. Karl Claxton & Larry F. Lacey & Stephen G. Walker, 2000. "Selecting treatments: a decision theoretic approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(2), pages 211-225.
    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.
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    1. Peter Bayer & Joel S. Brown & Johan Dubbeldam & Mark Broom, 2022. "A Markovian decision model of adaptive cancer treatment and quality of life," Post-Print hal-03767027, HAL.
    2. Bonny Parkinson & Sallie-Anne Pearson & Rosalie Viney, 2014. "Economic evaluations of trastuzumab in HER2-positive metastatic breast cancer: a systematic review and critique," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(1), pages 93-112, January.
    3. Hua Zhang & Mingdong Huo & Jianqian Chao & Pei Liu, 2016. "Application of Bayesian Approach to Cost-Effectiveness Analysis of Antiviral Treatments in Chronic Hepatitis B," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-9, August.
    4. McClean, Sally & Gillespie, Jennifer & Garg, Lalit & Barton, Maria & Scotney, Bryan & Kullerton, Ken, 2014. "Using phase-type models to cost stroke patient care across health, social and community services," European Journal of Operational Research, Elsevier, vol. 236(1), pages 190-199.
    5. K. Le Lay & E. Myon & S. Hill & L. Riou-Franca & D. Scott & M. Sidhu & D. Dunlop & R. Launois, 2007. "Comparative cost-minimisation of oral and intravenous chemotherapy for first-line treatment of non-small cell lung cancer in the UK NHS system," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 8(2), pages 145-151, June.
    6. 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.

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