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Dimensions of design space: a decision-theoretic approach to optimal research design


  • Stefano Conti

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

  • Karl Claxton

    (Centre for Health Economics, University of York and Department of Economics and Related Studies, University of York, UK.)


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 pay-off to proposed research is achieved when its sample sizes, and allocation between available treatment options, are chosen to maximise 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: i) a single trial of all the parameters; ii) a clinical trial providing evidence only on clinical endpoints; iii) an epidemiological study of natural history of disease and iv) a survey of quality of life. The possible combinations, samples sizes and allocation between trial arms are evaluated over a range of costeffectiveness thresholds. The computational challenges are addressed by implementing optimisation algorithms to search the ENBS surface more efficiently over such large dimensions.

Suggested Citation

  • Stefano Conti & Karl Claxton, 2008. "Dimensions of design space: a decision-theoretic approach to optimal research design," Working Papers 038cherp, Centre for Health Economics, University of York.
  • Handle: RePEc:chy:respap:38cherp

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    References listed on IDEAS

    1. Palmer, Stephen & Smith, Peter C., 2000. "Incorporating option values into the economic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 19(5), pages 755-766, September.
    2. 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.
    3. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Aaron A. Stinnett & John Mullahy, 1998. "Net Health Benefits: A New Framework for the Analysis of Uncertainty in Cost-Effectiveness Analysis," NBER Technical Working Papers 0227, National Bureau of Economic Research, Inc.
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    Bayesian decision theory; expected value of information; research design; costeffectiveness analysis;

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