IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v68y2019i2p385-410.html
   My bibliography  Save this article

AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials

Author

Listed:
  • Jiaying Lyu
  • Yuan Ji
  • Naiqing Zhao
  • Daniel V. T. Catenacci

Abstract

We propose a flexible design for the identification of optimal dose combinations in dual‐agent dose finding clinical trials. The design is called AAA, standing for three adaptations: adaptive model selection, adaptive dose insertion and adaptive cohort division. The adaptations highlight the need and opportunity for innovation for dual‐agent dose finding and are supported by the numerical results presented in the proposed simulation studies. To our knowledge, this is the first design that allows for all three adaptations at the same time. We find that AAA enhances the chance of finding the optimal dose combinations and shortens the trial duration. A clinical trial is being planned to apply the AAA design and a Web tool is being developed for both statisticians and non‐statisticians.

Suggested Citation

  • Jiaying Lyu & Yuan Ji & Naiqing Zhao & Daniel V. T. Catenacci, 2019. "AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(2), pages 385-410, February.
    • Handle: RePEc:bla:jorssc:v:68:y:2019:i:2:p:385-410
    DOI: 10.1111/rssc.12291
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12291
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssc.12291?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:jorssc:v:68:y:2019:i:2:p:385-410. 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.