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Valuing Trial Designs from a Pharmaceutical Perspective Using Value‐Based Pricing

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  • Penny Breeze
  • Alan Brennan

Abstract

Our aim was to adapt the traditional framework for expected net benefit of sampling (ENBS) to be more compatible with drug development trials from the pharmaceutical perspective. We modify the traditional framework for conducting ENBS and assume that the price of the drug is conditional on the trial outcomes. We use a value‐based pricing (VBP) criterion to determine price conditional on trial data using Bayesian updating of cost‐effectiveness (CE) model parameters. We assume that there is a threshold price below which the company would not market the new intervention. We present a case study in which a phase III trial sample size and trial duration are varied. For each trial design, we sampled 10 000 trial outcomes and estimated VBP using a CE model. The expected commercial net benefit is calculated as the expected profits minus the trial costs. A clinical trial with shorter follow‐up, and larger sample size, generated the greatest expected commercial net benefit. Increasing the duration of follow‐up had a modest impact on profit forecasts. Expected net benefit of sampling can be adapted to value clinical trials in the pharmaceutical industry to optimise the expected commercial net benefit. However, the analyses can be very time consuming for complex CE models. © 2014 The Authors. Health Economics published by John Wiley & Sons Ltd.

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  • 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.
    • Handle: RePEc:wly:hlthec:v:24:y:2015:i:11:p:1468-1482
    DOI: 10.1002/hec.3103
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    References listed on IDEAS

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    1. Samer A. Kharroubi & Alan Brennan & Mark Strong, 2011. "Estimating Expected Value of Sample Information for Incomplete Data Models Using Bayesian Approximation," Medical Decision Making, , vol. 31(6), pages 839-852, November.
    2. 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.
    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. Claire McKenna & Karl Claxton, 2011. "Addressing Adoption and Research Design Decisions Simultaneously," Medical Decision Making, , vol. 31(6), pages 853-865, November.
    5. 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.
    6. 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.
    7. 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.
    8. Martin Hoyle, 2011. "Accounting for the Drug Life Cycle and Future Drug Prices in Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 29(1), pages 1-15, January.
    9. 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.
    10. Andrew Willan & Simon Eckermann, 2012. "Value of Information and Pricing New Healthcare Interventions," PharmacoEconomics, Springer, vol. 30(6), pages 447-459, June.
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