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Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design

Author

Listed:
  • Stefano Conti

    (Centre for Health Economics, University of York, York, UK, Stefano.Conti@hpa.org.uk)

  • Karl Claxton

    (Department of Economics and Related Studies, University of York, York, UK, Received 25 September 2008 from the Centre for Health Economics)

Abstract

Bayesian decision theory can be used not only to establish the optimal sample size and its allocation in a single clinical study but also to identify an optimal portfolio of research combining different types of study design. Within a single study, the highest societal payoff to proposed research is achieved when its sample sizes and allocation between available treatment options are chosen to maximize the expected net benefit of sampling (ENBS). Where a number of different types of study informing different parameters in the decision problem could be conducted, the simultaneous estimation of ENBS across all dimensions of the design space is required to identify the optimal sample sizes and allocations within such a research portfolio. This is illustrated through a simple example of a decision model of zanamivir for the treatment of influenza. The possible study designs include: 1) a single trial of all the parameters, 2) a clinical trial providing evidence only on clinical endpoints, 3) an epidemiological study of natural history of disease, and 4) a survey of quality of life. The possible combinations, samples sizes, and allocation between trial arms are evaluated over a range of cost-effectiveness thresholds. The computational challenges are addressed by implementing optimization algorithms to search the ENBS surface more efficiently over such large dimensions.

Suggested Citation

  • Stefano Conti & Karl Claxton, 2009. "Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design," Medical Decision Making, , vol. 29(6), pages 643-660, November.
    • Handle: RePEc:sae:medema:v:29:y:2009:i:6:p:643-660
    DOI: 10.1177/0272989X09336142
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    References listed on IDEAS

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    1. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    2. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    3. Brennan, Alan & Kharroubi, Samer A., 2007. "Efficient computation of partial expected value of sample information using Bayesian approximation," Journal of Health Economics, Elsevier, vol. 26(1), pages 122-148, January.
    4. 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.
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    Citations

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    Cited by:

    1. Karl Claxton & Elisabeth Fenwick & Mark J. Sculpher, 2012. "Decision-making with Uncertainty: The Value of Information," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 51, Edward Elgar Publishing.
    2. Simon Walker & Mark Sculpher & Karl Claxton & Steve Palmer, 2012. "Coverage with evidence development, only in research, risk sharing or patient access scheme? A framework for coverage decisions," Working Papers 077cherp, Centre for Health Economics, University of York.
    3. Anna Heath & Mark Strong & David Glynn & Natalia Kunst & Nicky J. Welton & Jeremy D. Goldhaber-Fiebert, 2022. "Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial," Medical Decision Making, , vol. 42(2), pages 143-155, February.
    4. Daniele Bregantini, 2014. "Don’t Stop ’Til You Get Enough: a quickest detection approach to HTA," Discussion Papers 14/04, Department of Economics, University of York.
    5. Anna Heath, 2022. "Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation," Medical Decision Making, , vol. 42(5), pages 626-636, July.
    6. Martin Forster & Paolo Pertile, 2013. "Optimal decision rules for HTA under uncertainty: a wider, dynamic perspective," Health Economics, John Wiley & Sons, Ltd., vol. 22(12), pages 1507-1514, December.

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