[1][2] Regression beta coefficient estimates from the Liang-Zeger GEE are consistent, unbiased, and asymptotically normal even when the working correlation is misspecified, under mild regularity conditions.
GEE is higher in efficiency than generalized linear models (GLMs) in the presence of high autocorrelation.
[1] Huber-White standard errors improve the efficiency of Liang-Zeger GEE in the absence of serial autocorrelation but may remove the marginal interpretation.
[4] Several independent formulations of these standard error estimators contribute to GEE theory.
Placing the independent standard error estimators under the umbrella term "GEE" may exemplify abuse of terminology.
GEEs belong to a class of regression techniques that are referred to as semiparametric because they rely on specification of only the first two moments.
They are a popular alternative to the likelihood-based generalized linear mixed model which is more at risk for consistency loss at variance structure specification.
[5] The trade-off of variance-structure misspecification and consistent regression coefficient estimates is loss of efficiency, yielding inflated Wald test p-values as a result of higher variance of standard errors than that of the most optimal.
The Hessian of the solution to the GEEs in the parameter space can be used to calculate robust standard error estimates.
The term "variance structure" refers to the algebraic form of the covariance matrix between outcomes, Y, in the sample.
Examples of variance structure specifications include independence, exchangeable, autoregressive, stationary m-dependent, and unstructured.
The most popular form of inference on GEE regression parameters is the Wald test using naive or robust standard errors, though the Score test is also valid and preferable when it is difficult to obtain estimates of information under the alternative hypothesis.
[9] This relationship is immediately obvious from the requirement that the score function satisfy the equation: