IDEAS home Printed from https://ideas.repec.org/a/spr/sankhb/v84y2022i1d10.1007_s13571-021-00261-2.html
   My bibliography  Save this article

A Curtailed Procedure for Selecting Among Treatments With Two Bernoulli Endpoints

Author

Listed:
  • Elena M. Buzaianu

    (University of North Florida)

  • Pinyuen Chen

    (Syracuse University)

  • Lifang Hsu

    (Le Moyne College)

Abstract

This paper is concerned with a closed adaptive sequential procedure for selecting a random-size subset containing experimental treatments that are better than a standard. All the k treatments under considerations are measured by two endpoints accounting for treatment efficacy and treatment safety respectively. The selection is made with regard to the two binary endpoints. An experimental treatment is considered to be better than the standard if its both endpoints have successful rates higher than the standard ones. We provide a step-by-step sampling rule, stopping rule, and decision rule for the proposed procedure. We show that the proposed sequential procedure achieves the same requirements for the probability of a correct selection as does the fixed-sample-size procedure, but requires fewer observations. We use simulations to evaluate the sample size savings of the proposed procedure over the corresponding fixed-sample-size procedure.

Suggested Citation

  • Elena M. Buzaianu & Pinyuen Chen & Lifang Hsu, 2022. "A Curtailed Procedure for Selecting Among Treatments With Two Bernoulli Endpoints," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 320-339, May.
  • Handle: RePEc:spr:sankhb:v:84:y:2022:i:1:d:10.1007_s13571-021-00261-2
    DOI: 10.1007/s13571-021-00261-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13571-021-00261-2
    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/s13571-021-00261-2?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.

    Citations

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


    Cited by:

    1. Juhee Lee & Peter F. Thall & Pavlos Msaouel, 2023. "Bayesian treatment screening and selection using subgroup‐specific utilities of response and toxicity," Biometrics, The International Biometric Society, vol. 79(3), pages 2458-2473, September.

    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:sankhb:v:84:y:2022:i:1:d:10.1007_s13571-021-00261-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.