IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v177y2014i2p419-438.html
   My bibliography  Save this article

Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies

Author

Listed:
  • Paolo Pertile
  • Martin Forster
  • Davide La Torre

Abstract

type="main" xml:id="rssa12025-abs-0001"> We present a Bayes sequential economic evaluation model for health technologies in which an investigator has flexibility over the timing of a decision to stop carrying out research and to conclude that one technology is preferred to another on cost-effectiveness grounds. We implement the model by using an evaluation of the treatment of bacterial sinusitis and derive approximations of the optimal stopping rule as a function of accumulated sample size. We compare the performance of the model with existing frequentist and Bayes sequential designs and investigate the sensitivity of the stopping rule to changes in the parameters of the model. Our results suggest that accounting for the dynamic nature of experimentation, together with its economic parameters, should lead to greater efficiency in resource allocation within healthcare systems.

Suggested Citation

  • Paolo Pertile & Martin Forster & Davide La Torre, 2014. "Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 419-438, February.
    • Handle: RePEc:bla:jorssa:v:177:y:2014:i:2:p:419-438
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-2
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stephen Chick & Martin Forster & Paolo Pertile, 2017. "A Bayesian decision theoretic model of sequential experimentation with delayed response," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1439-1462, November.
    2. Levaggi, Laura & Levaggi, Rosella, 2024. "Competition, value-based prices and incentives to research personalised drugs," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    3. Oluwaseun Sharomi & Tufail Malik, 2017. "Optimal control in epidemiology," Annals of Operations Research, Springer, vol. 251(1), pages 55-71, April.
    4. 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.
    5. Andres Alban & Stephen E. Chick & Martin Forster, 2023. "Value-Based Clinical Trials: Selecting Recruitment Rates and Trial Lengths in Different Regulatory Contexts," Management Science, INFORMS, vol. 69(6), pages 3516-3535, June.
    6. 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.
    7. Williamson, S. Faye & Jacko, Peter & Jaki, Thomas, 2022. "Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    8. Panos Kouvelis & Joseph Milner & Zhili Tian, 2017. "Clinical Trials for New Drug Development: Optimal Investment and Application," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 437-452, July.
    9. Stephen E. Chick & Noah Gans & Özge Yapar, 2022. "Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions," Management Science, INFORMS, vol. 68(7), pages 4919-4938, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:177:y:2014:i:2:p:419-438. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.