IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v50y2001i1p43-61.html
   My bibliography  Save this article

A comparison of models for clustered binary outcomes: analysis of a designed immunology experiment

Author

Listed:
  • Rebecca A. Betensky
  • Paige L. Williams
  • Howard M. Lederman

Abstract

The lymphocyte proliferative assay (LPA) of immune competence was conducted on 52 subjects, with up to 36 processing conditions per subject, to evaluate whether samples could be shipped or stored overnight, rather than being processed on fresh blood as currently required. The LPA study resulted in clustered binary data, with both cluster level and cluster‐varying covariates. Two modelling strategies for the analysis of such clustered binary data are through the cluster‐specific and population‐averaged approaches. Whereas most research in this area has focused on the analysis of matched pairs data, in many situations, such as the LPA study, cluster sizes are naturally larger. Through considerations of interpretation and efficiency of these models when applied to large clusters, the mixed effect cluster‐specific model was selected as most appropriate for the analysis of the LPA data. The model confirmed that the LPA response is significantly impaired in individuals infected with the human immunodeficiency virus (HIV). The LPA response was found to be significantly lower for shipped and overnight samples than for fresh samples, and this effect was significantly stronger among HIV‐infected individuals. Surprisingly, an anticoagulant effect was not detected.

Suggested Citation

  • Rebecca A. Betensky & Paige L. Williams & Howard M. Lederman, 2001. "A comparison of models for clustered binary outcomes: analysis of a designed immunology experiment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(1), pages 43-61.
  • Handle: RePEc:bla:jorssc:v:50:y:2001:i:1:p:43-61
    DOI: 10.1111/1467-9876.00219
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9876.00219
    Download Restriction: no

    File URL: https://libkey.io/10.1111/1467-9876.00219?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
    ---><---

    Citations

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


    Cited by:

    1. Mariangela Sciandra & Vito Muggeo & Gianfranco Lovison, 2008. "Subject-specific odds ratios in binomial GLMMs with continuous response," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(3), pages 309-320, July.

    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:50:y:2001:i:1:p:43-61. 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: 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.