IDEAS home Printed from
   My bibliography  Save this article

Model diagnostic plots for repeated measures data using the generalized estimating equations approach


  • Oh, Sohee
  • Carriere, K.C.
  • Park, Taesung


The generalized estimating equations (GEE) approach has been widely used to analyze repeated measures data. However, in the absence of likelihood ratio tests, model diagnostic checking tools are not well established for the GEE approach, whereas they are for other likelihood-based approaches. Diagnostic checking tools are essential for determining a model's goodness of fit, especially for non-normal data. In this paper, we propose simple residual plots to investigate the goodness of fit of the model based on the GEE approach for discrete data. The proposed residual plots are based on the quantile-quantile (Q-Q) plots of a [chi]2-distribution, and are particularly useful for comparing several models simultaneously.

Suggested Citation

  • Oh, Sohee & Carriere, K.C. & Park, Taesung, 2008. "Model diagnostic plots for repeated measures data using the generalized estimating equations approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 222-232, September.
  • Handle: RePEc:eee:csdana:v:53:y:2008:i:1:p:222-232

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Park, Taesung & Davis, Charles S. & Li, Ning, 1998. "Alternative Gee estimation procedures for discrete longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 28(3), pages 243-256, September.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Vens, Maren & Ziegler, Andreas, 2012. "Generalized estimating equations and regression diagnostics for longitudinal controlled clinical trials: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1232-1242.

    More about this item


    Access and download statistics


    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:eee:csdana:v:53:y:2008:i:1:p:222-232. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.