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Expected Net Present Value of Sample Information: from burden to investment

Listed author(s):
  • Peter S Hall


    (Academic Unit of Health Economics, Leeds institute of Health Sciences, University of Leeds.)

  • Richard Edlin

    (Academic Unit of Health Economics, Leeds institute of Health Sciences, University of Leeds.)

  • Samer Kharroubi

    (Department of Mathematics, University of York.)

  • Walter Gregory

    (Clinical Trials Research Unit, University of Leeds)

  • Christopher McCabe

    (Academic Unit of Health Economics, Leeds institute of Health Sciences, University of Leeds)

The Expected Value of Information Framework has been proposed as a method for identifying when health care technologies should be reimbursed and when reimbursement should be withheld awaiting more evidence. The standard framework assesses the value of having additional evidence available to inform a current reimbursement decision. This can be thought of as the burden of not having the additional evidence available at the time of the decision. However, the information that decision makers need to decide whether to reimburse now or await more evidence is the value of investing in the creation of the new evidence to inform a future decision. Assessing the value requires the analysis to incorporate the costs of the research, the time it will take for the research to report and what happens to patients whilst the research is undertaken and once it has reported. In this paper we describe a development of the calculation of the expected value of sample information that assesses the value of investing in further research using (a) an only-in-research strategy; and (b) an only-with-research strategy.

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File Function: First version, 2011
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Paper provided by Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds in its series Working Papers with number 1101.

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Length: 25 pages
Date of creation: 2011
Publication status: Published in Medical Decision Making, May-June 2012, Vol 32 no. 3, pages E11-E21
Handle: RePEc:lee:wpaper:1101
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  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. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769.
  3. J. Kirsch & A. McGuire, 2000. "Establishing health state valuations for disease specific states: an example from heart disease," Health Economics, John Wiley & Sons, Ltd., vol. 9(2), pages 149-158.
  4. 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.
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