IDEAS home Printed from
   My bibliography  Save this article

Comprehensive decision analytical modelling in economic evaluation: a Bayesian approach


  • Nicola J. Cooper
  • Alex J. Sutton
  • Keith R. Abrams
  • David Turner
  • Allan Wailoo


Decision analytical models are widely used in economic evaluation of health care interventions with the objective of generating valuable information to assist health policy decision‐makers to allocate scarce health care resources efficiently. The whole decision modelling process can be summarised in four stages: (i) a systematic review of the relevant data (including meta‐analyses), (ii) estimation of all inputs into the model (including effectiveness, transition probabilities and costs), (iii) sensitivity analysis for data and model specifications, and (iv) evaluation of the model. The aim of this paper is to demonstrate how the individual components of decision modelling, outlined above, may be addressed simultaneously in one coherent Bayesian model (sometimes known as a comprehensive decision analytical model) and evaluated using Markov Chain Monte Carlo simulation implemented in the specialist software WinBUGS. To illustrate the method described, it is applied to two illustrative examples: (1) The prophylactic use of neurominidase inhibitors for the prevention of influenza. (2) The use of taxanes for the second‐line treatment of advanced breast cancer. The advantages of integrating the four stages outlined into one comprehensive decision analytical model, compared to the conventional ‘two‐stage’ approach, are discussed. Copyright © 2003 John Wiley & Sons, Ltd.

Suggested Citation

  • Nicola J. Cooper & Alex J. Sutton & Keith R. Abrams & David Turner & Allan Wailoo, 2004. "Comprehensive decision analytical modelling in economic evaluation: a Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(3), pages 203-226, March.
  • Handle: RePEc:wly:hlthec:v:13:y:2004:i:3:p:203-226
    DOI: 10.1002/hec.804

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Claxton, K. & Thompson, K. M., 2001. "A dynamic programming approach to the efficient design of clinical trials," Journal of Health Economics, Elsevier, vol. 20(5), pages 797-822, September.
    2. N. Neymark & I. Adriaenssen & T. Gorlia & S. Caleo & M. Bolla, 2002. "Estimating survival gain for economic evaluations with survival time as principal endpoint: A cost‐effectiveness analysis of adding early hormonal therapy to radiotherapy in patients with locally adva," Health Economics, John Wiley & Sons, Ltd., vol. 11(3), pages 233-248, April.
    3. Karl Claxton, 1999. "Bayesian approaches to the value of information: implications for the regulation of new pharmaceuticals," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 269-274, May.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Richard Grieve & John Cairns & Simon G. Thompson, 2010. "Improving costing methods in multicentre economic evaluation: the use of multiple imputation for unit costs," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 939-954, August.
    2. Christopher H. Jackson & Linda D. Sharples & Simon G. Thompson, 2010. "Structural and parameter uncertainty in Bayesian cost‐effectiveness models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 233-253, March.
    3. Zoie Shui-Yee Wong & David Goldsman & Kwok-Leung Tsui, 2016. "Economic Evaluation of Individual School Closure Strategies: The Hong Kong 2009 H1N1 Pandemic," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-18, January.
    4. Mark J. Sculpher & Karl Claxton & Mike Drummond & Chris McCabe, 2006. "Whither trial‐based economic evaluation for health care decision making?," Health Economics, John Wiley & Sons, Ltd., vol. 15(7), pages 677-687, July.
    5. Zafar Zafari & Kristian Thorlund & J. FitzGerald & Carlo Marra & Mohsen Sadatsafavi, 2014. "Network vs. Pairwise Meta-Analyses: A Case Study of the Impact of an Evidence-Synthesis Paradigm on Value of Information Outcomes," PharmacoEconomics, Springer, vol. 32(10), pages 995-1004, October.
    6. J. Brown & N. J. Welton & C. Bankhead & S. H. Richards & L. Roberts & C. Tydeman & T. J. Peters, 2006. "A Bayesian approach to analysing the cost‐effectiveness of two primary care interventions aimed at improving attendance for breast screening," Health Economics, John Wiley & Sons, Ltd., vol. 15(5), pages 435-445, May.
    7. Andrew Briggs, 2012. "Statistical Methods for Cost-effectiveness Analysis Alongside Clinical Trials," Chapters, in: Andrew M. Jones (ed.),The Elgar Companion to Health Economics, Second Edition, chapter 50, Edward Elgar Publishing.
    8. Richard M. Nixon & David Wonderling & Richard D. Grieve, 2010. "Non‐parametric methods for cost‐effectiveness analysis: the central limit theorem and the bootstrap compared," Health Economics, John Wiley & Sons, Ltd., vol. 19(3), pages 316-333, March.
    9. Marta O Soares & L Canto e Castro, 2010. "Simulation or cohort models? Continuous time simulation and discretized Markov models to estimate cost-effectiveness," Working Papers 056cherp, Centre for Health Economics, University of York.
    10. Alexei Botchkarev, 2016. "Essential notion of the health economic evaluation: Definition," Economic Analysis Working Papers (2002-2010). Atlantic Review of Economics (2011-2016), Colexio de Economistas de A Coruña, Spain and Fundación Una Galicia Moderna, vol. 2, pages 1-1, December.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:hlthec:v:13:y:2004:i:3:p:203-226. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.