text: <li>Is there a way to study population average effects of a covariate against specific individual effects?</li>
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  *Is there a way to study population average effects of a covariate against specific individual effects?
  ==Overview==
  ==Overview==
  Revision as of 08:44, 23 May 2016
  Contents
  1 Model-based Analytics - Generalized Estimating Equation (GEE) Modeling
  2 Questions
  3 Overview
  4 See also
  Model-based Analytics - Generalized Estimating Equation (GEE) Modeling
  Questions
  How to represent dependencies in linear models and examine causal effects?
  Is there a way to study population average effects of a covariate against specific individual effects?
  Overview
  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.
  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.