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Optimal decision rules for HTA under uncertainty: a wider, dynamic perspective

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  • Martin Forster
  • Paolo Pertile

Abstract

We present a two‐period framework, which combines real option and decision‐theoretic approaches to health technology assessment under uncertainty. By viewing adoption, treatment and research decisions as a single economic project, we illustrate how their key dimensions affect optimal rules. We consider the results in relation to the existing literature and argue that developments in this direction could contribute substantially to efficiency gains in resource allocation. Copyright © 2012 John Wiley & Sons, Ltd.

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  • Martin Forster & Paolo Pertile, 2013. "Optimal decision rules for HTA under uncertainty: a wider, dynamic perspective," Health Economics, John Wiley & Sons, Ltd., vol. 22(12), pages 1507-1514, December.
    • Handle: RePEc:wly:hlthec:v:22:y:2013:i:12:p:1507-1514
    DOI: 10.1002/hec.2893
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    Cited by:

    1. Daniele Bregantini & Jacco J.J. Thijssen, 2014. "On a simple quickest detection rule for health-care technology assessment," Discussion Papers 14/01, Department of Economics, University of York.
    2. Afschin Gandjour, 2015. "A model to optimize investments in health technologies, quality of care and research," Applied Economics, Taylor & Francis Journals, vol. 47(20), pages 2031-2039, April.
    3. 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.
    4. Daniele Bregantini, 2014. "Don’t Stop ’Til You Get Enough: a quickest detection approach to HTA," Discussion Papers 14/04, Department of Economics, University of York.

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