IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0333992.html
   My bibliography  Save this article

Generalized estimating equations for modeling cluster randomized trial data on smoking cessation among tuberculosis patients

Author

Listed:
  • Vasantha Mahalingam
  • Ratnakar Singh
  • Ramesh Kumar Santhanakrishnan
  • Adhin Bhaskar
  • Ponnuraja Chinnaiyan

Abstract

There is a paucity of studies applying Generalized Estimating Equations (GEE) for longitudinal analysis of smoking cessation outcomes within the framework of a cluster randomized trial, especially among tuberculosis (TB) patients. In this study, a GEE model which accounts for repeated measures and cluster-level effects was implemented to identify factors associated with smoking cessation among TB patients. The data included 375 TB patients who were smokers and given TB treatment during 2013–2016 in Kanchipuram and Villupuram districts under a cluster randomized trial. GEE modeling provided robust, population-averaged estimates while accounting for intra-cluster correlation, confirming the sustained impact of these interventions. The model demonstrated that smoking cessation interventions, when integrated with TB treatment, had an impact on cessation outcomes in these populations.

Suggested Citation

  • Vasantha Mahalingam & Ratnakar Singh & Ramesh Kumar Santhanakrishnan & Adhin Bhaskar & Ponnuraja Chinnaiyan, 2025. "Generalized estimating equations for modeling cluster randomized trial data on smoking cessation among tuberculosis patients," PLOS ONE, Public Library of Science, vol. 20(10), pages 1-12, October.
  • Handle: RePEc:plo:pone00:0333992
    DOI: 10.1371/journal.pone.0333992
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0333992
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0333992&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0333992?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:plo:pone00:0333992. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.