IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v44y1995i2p173-186.html
   My bibliography  Save this article

Analysing Categorical Responses Obtained from Large Clusters

Author

Listed:
  • Michael E. Miller

Abstract

Several researchers have proposed generalized estimating equation (GEE) approaches for the analysis of clustered binary or multicategory response data. When large numbers of observations are taken within clusters, these GEE methods can require inversion of extremely large covariance matrices. However, in many applications, only covariates specific to large groups of observations within the cluster are of interest for the marginal model, whereas covariates specific to individual measurements within the cluster are important in modelling the pairwise associations. In this paper, we use two medical examples to motivate a discussion illustrating that, when the marginal model contains only covariates specific to many observations within a cluster, the estimating equations used for the marginal analyses can be formulated in terms of proportions rather than binary indicator variables, while still modelling the pairwise associations with covariates specific to individual measurements within the cluster. This approach can reduce the computational burden that is inherent in the analysis of large clusters, while still allowing the potential gains in efficiency for the marginal analysis that can be obtained by modelling the pairwise associations.

Suggested Citation

  • Michael E. Miller, 1995. "Analysing Categorical Responses Obtained from Large Clusters," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(2), pages 173-186, June.
    • Handle: RePEc:bla:jorssc:v:44:y:1995:i:2:p:173-186
    DOI: 10.2307/2986343
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2986343
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2986343?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:bla:jorssc:v:44:y:1995:i:2:p:173-186. 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.