IDEAS home Printed from https://ideas.repec.org/p/lee/wpaper/1505.html
   My bibliography  Save this paper

A Sequential Approach to Combined Clinical Trial and Health Technology Adoption Decisions

Author

Listed:
  • Jacco Thijssen

    (The York Management School, University of York)

  • Daniele Bergantini

    (Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds)

Abstract

We introduce a model to sequentially analyse both clinical trials and cost effectiveness of a new health technology. This provides a consistent decision-making framework for evaluating (i) evidence from clinical trials, (ii) the expected value of further trials, (iii) the costs and benefits of adoption/abandonment. We derive the optimal decision rule by appropriately extending the Bayesian framework of sequential hypothesis testing. We find that increased noise in the trial observations lowers the value of the new technology, but leads to decisions, in expectation, being taken faster. The expected total discounted costs of the trial are non-monotonic in the incremental trial costs. The proposed method numerically outperforms a frequentist approach in terms of total value, and expected trial duration and costs. Delays in trial observations can have big qualitative effects on value. The model is illustrated using data on standard versus robot-assisted laporascopic prostatectomy.

Suggested Citation

  • Jacco Thijssen & Daniele Bergantini, 2015. "A Sequential Approach to Combined Clinical Trial and Health Technology Adoption Decisions," Working Papers 1505, Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds.
    • Handle: RePEc:lee:wpaper:1505
    as

    Download full text from publisher

    File URL: http://medhealth.leeds.ac.uk/downloads/file/2162/auhe_wp1505
    File Function: First version, 2015
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    optimal stopping; clinical trials; health technology assessment;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:lee:wpaper:1505. 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: Judy Wright (email available below). General contact details of provider: https://edirc.repec.org/data/heleeuk.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.