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Costly sequential experimentation and project valuation with an application to health technology assessment

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  • Thijssen, Jacco J.J.
  • Bregantini, Daniele

Abstract

We study the optimal investment/abandonment decision for a project, where costly sequential experimentation provides information about its true profitability. We derive the optimal decision rule by appropriately extending the Bayesian framework of sequential hypothesis testing. The optimal decision time takes the form of the first exit time of a particular inaction region. We find that increased noise in the observations lowers the value of the project, and that the effect on the expected time at which a decision is taken is ambiguous. Delays in observations affect both project value and the inaction region. The model is illustrated with a health technology assessment application using data on standard versus robot-assisted laporascopic prostatectomy.

Suggested Citation

  • Thijssen, Jacco J.J. & Bregantini, Daniele, 2017. "Costly sequential experimentation and project valuation with an application to health technology assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 202-229.
  • Handle: RePEc:eee:dyncon:v:77:y:2017:i:c:p:202-229
    DOI: 10.1016/j.jedc.2017.01.016
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    Cited by:

    1. Chen, Peng-Ting, 2018. "Medical big data applications: Intertwined effects and effective resource allocation strategies identified through IRA-NRM analysis," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 150-164.
    2. Itamar Megiddo & Dusan Drabik & Tim Bedford & Alec Morton & Justus Wesseler & Ramanan Laxminarayan, 2019. "Investing in antibiotics to alleviate future catastrophic outcomes: What is the value of having an effective antibiotic to mitigate pandemic influenza?," Health Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 556-571, April.
    3. Sebastian Sund & Lars H. Sendstad & Jacco J. J. Thijssen, 2022. "Kalman filter approach to real options with active learning," Computational Management Science, Springer, vol. 19(3), pages 457-490, July.

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