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Assessment of modeling longitudinal binary data based on graphical methods

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  • Kuo-Chin Lin
  • Yi-Ju Chen

Abstract

Longitudinal categorical data are commonly applied in a variety of fields and are frequently analyzed by generalized estimating equation (GEE) method. Prior to making further inference based on the GEE model, the assessment of model fit is crucial. Graphical techniques have long been in widespread use for assessing the model adequacy. We develop alternative graphical approaches utilizing plots of marginal model-checking condition and local mean deviance to assess the GEE model with logit link for longitudinal binary responses. The applications of the proposed procedures are illustrated through two longitudinal binary datasets.

Suggested Citation

  • Kuo-Chin Lin & Yi-Ju Chen, 2017. "Assessment of modeling longitudinal binary data based on graphical methods," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(7), pages 3426-3437, April.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:7:p:3426-3437
    DOI: 10.1080/03610926.2015.1062107
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