What is Xtgee?
xtgee fits population-averaged panel-data models. In particular, xtgee fits generalized linear models and allows you to specify the within-group correlation structure for the panels.
What is Xtlogit?
xtlogit fits random-effects, conditional fixed-effects, and population-averaged logit models for a binary dependent variable. The probability of a positive outcome is assumed to be determined by the logistic cumulative distribution function. Results may be reported as coefficients or odds ratios.
How are correlations estimated in a Gee model?
One possible correlation structure for these data would be stationary 3-dependence. This working model estimates three correlation parameters: the correlations at lag 1, lag 2 and lag 3. If there were sufficient data it would even be possible to estimate all 6 correlation parameters. This is known as the “saturated” working model.
How to model the within-subject covariance structure in Gee?
The very crux of GEE is instead of attempting to model the within-subject covariance structure, to treat it as a nuisance and simply model the mean response. In this framework, the covariance structure doesn’t need to be specified correctly for us to get reasonable estimates of regression coefficients and standard errors.
How are conditional and marginal effects related in Gee?
The conditional and marginal effects are the same for linear models, but for correlated data, the mixed model estimates intracluster correlations for weighted LS, and this gives you individual level SE estimates (GEE has population level SE estimates which are generally bigger).
How are Gee models used in real life?
GEE models in practice The models are used in the same way as standard generalised linear models, and the coefficients have the same interpretation. They measure differences in the response for a unit change in the predictor, averaged over the whole sample.