Abstract
The purpose of this paper is to describe and illustrate a regression approach to the analysis of correlated binary outcomes (Liang & Zeger, 1986). Ignoring the correlations between repeated observations can lead to invalid inferences. This approach extends logistic regression to account for repeated observations in each of a series of individuals. In this paper, I present a nontechnical introduction to the generalized estimating equations (GEE) approach. A fictitious example is used to demonstrate that GEE regression correctly adjusts for the correlations between repeated binary observations. The approach is illustrated with an analysis of safer sex practices among high-risk teenagers.
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The author thanks Gary Harper and Lisa Carver for the use of safer sex data, Gwowen Shieh, George Michel, and Sue O’Curry for comments on an earlier version of this paper, and Patrick Onghena and two anonymous referees for comments and suggestions, which resulted in a clearer exposition.
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Sheu, Cf. Regression analysis of correlated binary outcomes. Behavior Research Methods, Instruments, & Computers 32, 269–273 (2000). https://doi.org/10.3758/BF03207794
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DOI: https://doi.org/10.3758/BF03207794