The use of GEE for analyzing longitudinal binomial data: A primer using data from a tobacco intervention

Ji Hyun Lee, Thaddeus A. Herzog, Cathy D. Meade, Monica S. Webb, Thomas H. Brandon

Research output: Contribution to journalArticlepeer-review

71 Scopus citations


Longitudinal study designs in addictive behaviors research are common as researchers have focused increasingly on how various explanatory variables affect responses over time. In particular, such designs are used in intervention studies that have multiple follow-up points. These designs typically involve repeated measurement of participants' responses, and thus correlation within each participant is expected. Correct inferences can only be obtained by taking into account this within-participant correlation between repeated measurements, which can complicate the analysis of longitudinal data. In recent years, generalized estimating equations (GEE) has become a standard method for analyzing non-normal longitudinal data, yet it often is not utilized by addiction researchers. The goal of this article is to provide an overview of the GEE approach for analyzing correlated binary data for behavioral researchers, using data from an intervention study on the prevention of relapse to tobacco smoking.

Original languageEnglish (US)
Pages (from-to)187-193
Number of pages7
JournalAddictive Behaviors
Issue number1
StatePublished - Jan 2007
Externally publishedYes


  • Binary longitudinal data
  • GEE
  • Relapse-prevention-intervention
  • Statistical analysis
  • Tobacco relapse

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Clinical Psychology


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