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| − | ==Questions==
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| − | *How to represent dependencies in linear models and examine causal effects?
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| − | *Is there a way to study population average effects of a covariate against specific individual effects?
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| − | ==Overview==
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| − | GEE is a marginal longitudinal method that directly assesses the mean relations of interest (i.e., how the mean dependent variable changes over time), accounting for covariances among the observations within subjects, and getting a better estimate and valid significance tests of the relations. Thus, GEE estimates two different equations, (1) for the mean relations, and (2) for the covariance structure. An advantage of GEE over random-effect models is that it does not require the dependent variable to be normally distributed. However, a disadvantage of GEE is that it is less flexible and versatile – commonly employed algorithms for it require a small-to-moderate number of time points evenly (or approximately evenly) spaced, and similarly spaced across subjects. Nevertheless, it is a little more flexible than repeated-measure ANOVA because it permits some missing values and has an easy way to test for and model away the specific form of autocorrelation within subjects.
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| − | GEE is mostly used when the study is focused on uncovering the population average effect of a covariate vs. the individual specific effect. These two things are only equivalent for linear models, but not in non-linear models.
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| − | ==See also==
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| − | *[[SMHS_BigDataBigSci| Back to Model-based Analytics]]
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| − | *[[SMHS_BigDataBigSci_GCM| Back to Growth Curve Modeling ]]
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| − | *[[SMHS_BigDataBigSci_SEM| Back to Structural Equation Modeling (SEM)]]
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| − | <hr>
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| − | * SOCR Home page: http://www.socr.umich.edu
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| − | {{translate|pageName=http://wiki.socr.umich.edu/index.php?title=SMHS_BigDataBigSci_GEE}}
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