IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v71y2022i5p2014-2037.html
   My bibliography  Save this article

Bayesian modelling strategies for borrowing of information in randomised basket trials

Author

Listed:
  • Luke O. Ouma
  • Michael J. Grayling
  • James M. S. Wason
  • Haiyan Zheng

Abstract

Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early‐phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing randomised basket trials, information borrowing could be exploited in two ways; considering the commensurability of either the treatment effects or the outcomes specific to each of the treatment groups between the subtrials. In this article, we extend a previous analysis model based on distributional discrepancy for borrowing over the subtrial treatment effects (‘treatment effect borrowing’, TEB) to borrowing over the subtrial groupwise responses (‘treatment response borrowing’, TRB). Simulation results demonstrate that both modelling strategies provide substantial gains over an approach with no borrowing. TRB outperforms TEB especially when subtrial sample sizes are small on all operational characteristics, while the latter has considerable gains in performance over TRB when subtrial sample sizes are large, or the treatment effects and groupwise mean responses are noticeably heterogeneous across subtrials. Further, we notice that TRB, and TEB can potentially lead to different conclusions in the analysis of real data.

Suggested Citation

  • Luke O. Ouma & Michael J. Grayling & James M. S. Wason & Haiyan Zheng, 2022. "Bayesian modelling strategies for borrowing of information in randomised basket trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 2014-2037, November.
  • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:2014-2037
    DOI: 10.1111/rssc.12602
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssc.12602
    Download Restriction: no

    File URL: https://libkey.io/10.1111/rssc.12602?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
    ---><---

    References listed on IDEAS

    as
    1. Yiyi Chu & Ying Yuan, 2018. "BLAST: Bayesian latent subgroup design for basket trials accounting for patient heterogeneity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(3), pages 723-740, April.
    2. Steffen Ventz & William T. Barry & Giovanni Parmigiani & Lorenzo Trippa, 2017. "Bayesian response-adaptive designs for basket trials," Biometrics, The International Biometric Society, vol. 73(3), pages 905-915, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Sidi Wang & Kelley M. Kidwell & Satrajit Roychoudhury, 2023. "Dynamic enrichment of Bayesian small‐sample, sequential, multiple assignment randomized trial design using natural history data: a case study from Duchenne muscular dystrophy," Biometrics, The International Biometric Society, vol. 79(4), pages 3612-3623, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin Jin & Qianying Liu & Wei Zheng & Zhenming Shun & Tun Tun Lin & Lei Gao & Yingwen Dong, 2020. "A Bayesian Method for the Detection of Proof of Concept in Early Phase Oncology Studies with a Basket Design," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 167-179, July.
    2. Yujie Zhao & Rui (Sammi) Tang & Yeting Du & Ying Yuan, 2023. "A Bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints," Biometrics, The International Biometric Society, vol. 79(2), pages 1459-1471, June.
    3. Liyun Jiang & Lei Nie & Ying Yuan, 2023. "Elastic priors to dynamically borrow information from historical data in clinical trials," Biometrics, The International Biometric Society, vol. 79(1), pages 49-60, March.
    4. Massimiliano Russo & Steffen Ventz & Victoria Wang & Lorenzo Trippa, 2023. "Inference in response‐adaptive clinical trials when the enrolled population varies over time," Biometrics, The International Biometric Society, vol. 79(1), pages 381-393, March.

    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:71:y:2022:i:5:p:2014-2037. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.