An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals

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Institutional Logics and Institutional Pluralism: The Contestation of Care and Science Logics in Medical Education, 1967–2005 (2010, background): “A key advantage of GEE “resides in the unbiased estimation of population-averaged regression coeffi cients despite possible misspecifi cation of the correlation structure” (Ghisletta and Spini, 2004: 421).”
Latino Parent Involvement and School Success (2012, methodology): “GEE methods are desirable to use with longitudinal data and a binary outcome; it calculates robust standard errors while allowing flexibility in specifying the correlation structure within cases (Ghisletta & Spini, 2004; Zeger & Liang, 1986; Zorn, 2001).”
On the Unnecessary Ubiquity of Hierarchical Linear Modeling (2017, methodology): “Standard GEE is not a likelihood method and therefore likelihood-based corrections cannot be applied to data that are MAR (Ghisletta & Spini, 2004).”
From Child Welfare to Juvenile Justice (2013, methodology): “The advantage of using a GEE model is that it provides unbiased marginal (population average) regression coefficients regardless of the correlation structure of the errors (Ghisletta & Spini, 2004).”
Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations (2021, methodology): “GEEs have been popular in the fields of epidemiology and biostatistics (Ballinger, 2004; Ghisletta & Spini, 2004) though much less so in other fields (Bauer & Sterba, 2011; McNeish et al., 2017).”