What is Xtgee?

What is Xtgee?

Description. 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. Quick start. Population-averaged linear regression of y on x1 and x2.

What is a GEE model?

In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unknown correlation between outcomes. They are a popular alternative to the likelihood–based generalized linear mixed model which is more sensitive to variance structure specification.

What is the difference between GLM and GEE?

GEE is an extension of generalized linear models (GLM) for the analysis of longitudinal data. In this method, the correlation between measurements is modeled by assuming a working correlation matrix. Moreover, GLMM is an extension of GLM, inasmuch as it allows random effects in linear predictors.

Can GEE handle unbalanced?

Both GEE and CS can handle unbalanced data. GEE works well if you have data missing and it is missing completely at random (MCAR). Under this assumption the GEE approach provides consistent estimators of the regression coefficients and of their robust variances even if the assumed working correlation is misspecified.

What is a population averaged model?

Population average models typically use a generalized estimating equation (GEE) approach. These methods are used in place of basic regression approaches because the health of residents in the same neighborhood may be correlated, thus violating independence assumptions made by traditional regression procedures.

What is scale parameter in GEE?

The scale parameter is a biproduct of the estimation of the GEE model and is estimated for all possible family-link combinations. To ensure that it is, we multiply the resulting variance matrix by the estimated scale parameter and then set the estimate to one.

When should I use GLMM?

Generalized linear mixed models (GLMMs) estimate fixed and random effects and are especially useful when the dependent variable is binary, ordinal, count or quantitative but not normally distributed. They are also useful when the dependent variable involves repeated measures, since GLMMs can model autocorrelation.

When should we use GEE?

Is GEE a mixed model?

Mixed effect modeling allows both fixed (aka marginal) and random effects, while GEE modeling allows for fixed effects alone. In a GEE model, the variability is in effect treated as a nuisance factor that is adjusted for as a covariate, meaning the researcher cannot describe changes in variability.

Is exchangeable correlation more efficient than Independence Gee?

With small clusters, imbalanced design, and incomplete within-cluster confounder adjustment, exchangeable correlation may be more inefficient and biased relative than independence GEE. Those assumptions can be rather strong, too. However, when those assumptions are met, you get more efficient inference with the exchangeable.

What is the most realistic xtgee correlation structure?

More realistic would be “unstructured” and “ar” For an example of comparing correlation structures, see the section on estat correlation in the Manual Entry for xtgee postestimation Last edited by Steve Samuels; 25 Jul 2015, 21:18 .

What is the best way to measure correlation in Gee?

You can use variograms and lorellograms to visualize correlation in longitudinal and panel studies. Intracluster correlation is a good measurement of the extent of correlation within clusters. Correlation structure in GEE, unlike mixed models, does not affect the marginal parameter estimates (which you are estimating with GEE).

Why use exchangeable correlation structure for standard error?

There is the potential for cluster level confounders in these data, such as genetic propensity to tooth decay or community education funding, so for that reason, you will get better standard error estimates by using an exchangeable correlation structure.