IDEAS home Printed from https://ideas.repec.org/p/chy/respap/9cherp.html
   My bibliography  Save this paper

Defining and characterising structural uncertainty in decision analytic models

Author

Listed:
  • Laura Bojke

    () (Centre for Health Economics, University of York)

  • Karl Claxton

    () (Centre for Health Economics, University of York)

  • Stephen Palmer

    () (Centre for Health Economics, University of York)

  • Mark Sculpher

    () (Centre for Health Economics, University of York)

Abstract

An inappropriate structure for a decision analytic model can potentially invalidate estimates of cost-effectiveness and estimates of the value of further research. However, there are often a number of alternative and credible structural assumptions which can be made. Although it is common practice to acknowledge potential limitations in model structure, there is a lack of clarity about methods to characterize the uncertainty surrounding alternative structural assumptions and their contribution to decision uncertainty. A review of decision models commissioned by the NHS Health Technology Programme was undertaken to identify the types of model uncertainties described in the literature. A second review was undertaken to identify approaches to characterise these uncertainties. The assessment of structural uncertainty has received little attention in the health economics literature. A common method to characterise structural uncertainty is to compute results for each alternative model specification, and to present alternative results as scenario analyses. It is then left to decision maker to assess the credibility of the alternative structures in interpreting the range of results. The review of methods to explicitly characterise structural uncertainty identified two methods: 1) model averaging, where alternative models, with different specifications, are built, and their results averaged, using explicit prior distributions often based on expert opinion and 2) Model selection on the basis of prediction performance or goodness of fit. For a number of reasons these methods are neither appropriate nor desirable methods to characterize structural uncertainty in decision analytic models. When faced with a choice between multiple models, another method can be employed which allows structural uncertainty to be explicitly considered and does not ignore potentially relevant model structures. Uncertainty can be directly characterised (or parameterised) in the model itself. This method is analogous to model averaging on individual or sets of model inputs, but also allows the value of information associated with structural uncertainties to be resolved.

Suggested Citation

  • Laura Bojke & Karl Claxton & Stephen Palmer & Mark Sculpher, 2006. "Defining and characterising structural uncertainty in decision analytic models," Working Papers 009cherp, Centre for Health Economics, University of York.
  • Handle: RePEc:chy:respap:9cherp
    as

    Download full text from publisher

    File URL: http://www.york.ac.uk/media/che/documents/papers/researchpapers/rp9_structural_uncertainty_in_decision_analytic_models.pdf
    File Function: First version, 2006
    Download Restriction: no

    References listed on IDEAS

    as
    1. Zhao, Xiande & Xie, Jinxing & Leung, Janny, 2002. "The impact of forecasting model selection on the value of information sharing in a supply chain," European Journal of Operational Research, Elsevier, vol. 142(2), pages 321-344, October.
    2. van Noortwijk, Jan M. & Cooke, Roger M. & Kok, Matthijs, 1995. "A Bayesian failure model based on isotropic deterioration," European Journal of Operational Research, Elsevier, vol. 82(2), pages 270-282, April.
    3. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    4. Xiaotong Shen & Hsin-Cheng Huang & Jimmy Ye, 2004. "Inference After Model Selection," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 751-762, January.
    5. Granger, C.W.J. & Pesaran, M. H., 1999. "Economic and Statistical Measures of Forecast Accuracy," Cambridge Working Papers in Economics 9910, Faculty of Economics, University of Cambridge.
    6. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Lois G. Kim & Simon G. Thompson, 2010. "Uncertainty and validation of health economic decision models," Health Economics, John Wiley & Sons, Ltd., vol. 19(1), pages 43-55.
    2. Laura Burgers & William Redekop & Johan Severens, 2014. "Challenges in Modelling the Cost Effectiveness of Various Interventions for Cardiovascular Disease," PharmacoEconomics, Springer, vol. 32(7), pages 627-637, July.
    3. Hossein Haji Ali Afzali & Jonathan Karnon, 2015. "Exploring Structural Uncertainty in Model-Based Economic Evaluations," PharmacoEconomics, Springer, vol. 33(5), pages 435-443, May.

    More about this item

    Lists

    This item is featured on the following reading lists or Wikipedia pages:
    1. Technology Assessment

    Statistics

    Access and download statistics

    Corrections

    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:chy:respap:9cherp. 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: (Gill Forder). General contact details of provider: http://edirc.repec.org/data/chyoruk.html .

    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.