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Value of Information and Pricing New Healthcare Interventions

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  • Andrew Willan
  • Simon Eckermann

Abstract

Previous application of value-of-information methods to optimal clinical trial design have predominantly taken a societal decision-making perspective, implicitly assuming that healthcare costs are covered through public expenditure and trial research is funded by government or donation-based philanthropic agencies. In this paper, we consider the interaction between interrelated perspectives of a societal decision maker (e.g. the National Institute for Health and Clinical Excellence [NICE] in the UK) charged with the responsibility for approving new health interventions for reimbursement and the company that holds the patent for a new intervention. We establish optimal decision making from societal and company perspectives, allowing for trade-offs between the value and cost of research and the price of the new intervention. Given the current level of evidence, there exists a maximum (threshold) price acceptable to the decision maker. Submission for approval with prices above this threshold will be refused. Given the current level of evidence and the decision maker’s threshold price, there exists a minimum (threshold) price acceptable to the company. If the decision maker’s threshold price exceeds the company’s, then current evidence is sufficient since any price between the thresholds is acceptable to both. On the other hand, if the decision maker’s threshold price is lower than the company’s, then no price is acceptable to both and the company’s optimal strategy is to commission additional research. The methods are illustrated using a recent example from the literature. Copyright Springer International Publishing AG 2012

Suggested Citation

  • Andrew Willan & Simon Eckermann, 2012. "Value of Information and Pricing New Healthcare Interventions," PharmacoEconomics, Springer, vol. 30(6), pages 447-459, June.
  • Handle: RePEc:spr:pharme:v:30:y:2012:i:6:p:447-459
    DOI: 10.2165/11592250-000000000-00000
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    References listed on IDEAS

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    1. 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, November.
    2. Karl Claxton & Larry F. Lacey & Stephen G. Walker, 2000. "Selecting treatments: a decision theoretic approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(2), pages 211-225.
    3. 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, February.
    4. Simon Eckermann & Andrew R. Willan, 2008. "The Option Value of Delay in Health Technology Assessment," Medical Decision Making, , vol. 28(3), pages 300-305, May.
    5. N. J. Welton & A. E. Ades & D. M. Caldwell & T. J. Peters, 2008. "Research prioritization based on expected value of partial perfect information: a case‐study on interventions to increase uptake of breast cancer screening," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 807-841, October.
    6. 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.
    7. 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, February.
    8. 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, May.
    9. 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.
    10. 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.
    11. McCabe, C & Claxton, K & Culyer, AJ, 2008. "The NICE Cost-Effectiveness Threshold: What it is and What that Means," MPRA Paper 26466, University Library of Munich, Germany.
    12. A. E. Ades & G. Lu & K. Claxton, 2004. "Expected Value of Sample Information Calculations in Medical Decision Modeling," Medical Decision Making, , vol. 24(2), pages 207-227, March.
    13. 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. Penny Breeze & Alan Brennan, 2015. "Valuing Trial Designs from a Pharmaceutical Perspective Using Value‐Based Pricing," Health Economics, John Wiley & Sons, Ltd., vol. 24(11), pages 1468-1482, November.
    2. Nikki McCaffrey & Meera Agar & Janeane Harlum & Jonathon Karnon & David Currow & Simon Eckermann, 2015. "Better Informing Decision Making with Multiple Outcomes Cost-Effectiveness Analysis under Uncertainty in Cost-Disutility Space," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-19, March.

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