IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v50y2021i23p5607-5616.html
   My bibliography  Save this article

Sample size estimation for comparing rates of change in K-group repeated binary measurements studies

Author

Listed:
  • Jijia Wang
  • Song Zhang
  • Chul Ahn

Abstract

In clinical research, longitudinal trials are frequently conducted to evaluate the treatment effect by comparing trends in repeated measurements among different intervention groups. For such longitudinal trials, many researchers have developed the sample size estimation methods for comparison between two groups measurements. In contrast, relatively less attention has been paid to trials with K-group (K≥3) comparison. Jung and Ahn (2004) and Lou et al. (2017) derived the sample size formulas for comparing trends among K groups using the generalized estimating equations approach for repeated continuous and count outcomes, respectively. However, to the best of our knowledge, there has been no development in sample size calculation for binary outcomes in multi-arms trials. In this paper, we present a sample size formula for comparing trends in K-group repeated binary measurements that accommodates various missing patterns and correlation structures. Simulation results show that the proposed method performs well under a wide range of design parameter settings. We illustrate the proposed method through an example.

Suggested Citation

  • Jijia Wang & Song Zhang & Chul Ahn, 2021. "Sample size estimation for comparing rates of change in K-group repeated binary measurements studies," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(23), pages 5607-5616, December.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:23:p:5607-5616
    DOI: 10.1080/03610926.2020.1736302
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2020.1736302
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2020.1736302?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.

    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:taf:lstaxx:v:50:y:2021:i:23:p:5607-5616. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.