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Optimal clinical trial design using value of information methods with imperfect implementation


  • Andrew R. Willan

    (SickKids Research Institute and University of Toronto, Canada)

  • Simon Eckermann

    (Centre for Clinical Change and Health Care Research, Flinders University, Australia)


Traditional sample size calculations for randomized clinical trials are based on the tests of hypotheses and depend on somewhat arbitrarily chosen factors, such as type I and II errors rates and the smallest clinically important difference. In response to this, many authors have proposed the use of methods based on the value of information as an alternative. Previous attempts have assumed perfect implementation, i.e. if current evidence favors the new intervention and no new information is sought or expected, all future patients will receive it. A framework is proposed to allow for this assumption to be relaxed. The profound effect that this can have on the optimal sample size and expected net gain is illustrated on two recent examples. In addition, a model for assessing the value of implementation strategies is proposed and illustrated. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Andrew R. Willan & Simon Eckermann, 2010. "Optimal clinical trial design using value of information methods with imperfect implementation," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 549-561.
  • Handle: RePEc:wly:hlthec:v:19:y:2010:i:5:p:549-561
    DOI: 10.1002/hec.1493

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    References listed on IDEAS

    1. Simon Eckermann & Andrew R. Willan, 2007. "Expected value of information and decision making in HTA," Health Economics, John Wiley & Sons, Ltd., vol. 16(2), pages 195-209.
    2. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2005. "The value of implementation and the value of information: combined and uneven development," Working Papers 005cherp, Centre for Health Economics, University of York.
    3. 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.
    4. Simon Eckermann & Andrew R. Willan, 2009. "Globally optimal trial design for local decision making," Health Economics, John Wiley & Sons, Ltd., vol. 18(2), pages 203-216.
    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.
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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: The Elgar Companion to Health Economics, Second Edition, chapter 51 Edward Elgar Publishing.
    2. Jobjörnsson, Sebastian & Forster, Martin & Pertile, Paolo & Burman, Carl-Fredrik, 2016. "Late-stage pharmaceutical R&D and pricing policies under two-stage regulation," Journal of Health Economics, Elsevier, vol. 50(C), pages 298-311.

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