IDEAS home Printed from https://ideas.repec.org/a/spr/drugsa/v45y2022i9d10.1007_s40264-022-01208-w.html
   My bibliography  Save this article

BAHAMA: A Bayesian Hierarchical Model for the Detection of MedDRA®-Coded Adverse Events in Randomized Controlled Trials

Author

Listed:
  • Alma Revers

    (Amsterdam UMC location University of Amsterdam)

  • Michel H. Hof

    (Amsterdam UMC location University of Amsterdam)

  • Aeilko H. Zwinderman

    (Amsterdam UMC location University of Amsterdam)

Abstract

Introduction Patients participating in randomized controlled trials (RCTs) are susceptible to a wide range of different adverse events (AE) during the RCT. MedDRA® is a hierarchical standardization terminology to structure the AEs reported in an RCT. The lowest level in the MedDRA hierarchy is a single medical event, and every higher level is the aggregation of the lower levels. Method We propose a multi-stage Bayesian hierarchical Poisson model for estimating MedDRA-coded AE rate ratios (RRs). To deal with rare AEs, we introduce data aggregation at a higher level within the MedDRA structure and based on thresholds on incidence and MedDRA structure. Results With simulations, we showed the effects of this data aggregation process and the method's performance. Furthermore, an application to a real example is provided and compared with other methods. Conclusion We showed the benefit of using the full MedDRA structure and using aggregated data. The proposed model, as well as the pre-processing, is implemented in an R-package: BAHAMA.

Suggested Citation

  • Alma Revers & Michel H. Hof & Aeilko H. Zwinderman, 2022. "BAHAMA: A Bayesian Hierarchical Model for the Detection of MedDRA®-Coded Adverse Events in Randomized Controlled Trials," Drug Safety, Springer, vol. 45(9), pages 961-970, September.
    • Handle: RePEc:spr:drugsa:v:45:y:2022:i:9:d:10.1007_s40264-022-01208-w
    DOI: 10.1007/s40264-022-01208-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40264-022-01208-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40264-022-01208-w?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
    ---><---

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

    References listed on IDEAS

    as
    1. Guoqing Diao & Guanghan F. Liu & Donglin Zeng & William Wang & Xianming Tan & Joseph F. Heyse & Joseph G. Ibrahim, 2019. "Efficient methods for signal detection from correlated adverse events in clinical trials," Biometrics, The International Biometric Society, vol. 75(3), pages 1000-1008, September.
    Full references (including those not matched with items on IDEAS)

    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.

      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:spr:drugsa:v:45:y:2022:i:9:d:10.1007_s40264-022-01208-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/economics/journal/40264 .

      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.