Potential bias in generalized estimating equations linkage methods under incomplete information

Akaike's information criterion in generalized estimating equations.

Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is ...

Regression analysis under incomplete linkage

Generalized Estimating Equations

It is a privilege to be able to peruse the fine article by Ziegler and Vens [1], as well as the contributions made by nine discussants [2]. Over the last 25 years, generalized estimating equations (GEE) have seen an ever further spreading use. Nonetheless, it is a technique confronted with confusion and, at times, misunderstanding. The user must carefully read the technique’s manual. Let us hig...

A Generalized Estimating Equations

When inferences focus on population averages, one can directly model all of the marginal expectations E(Yij) = μij in terms of covariates of interest. This is typically done via h(μij) = x ′ ijβ, with h(·) some known link function, such as the logit link for binary responses. The marginal variance depends on the marginal mean according to Var(Yij) = v(μij)φ, where v(·) is a known variance funct...

Power under local alternatives for generalized estimating equations

This talk discusses the problem of calculating power in generalized estimating equation (GEE) settings that arise in biomedical studies involving clustered or correlated data (e.g., longitudinal studies and sibling studies). Existing approaches [e.g., Liu and Liang (1997) and Shih (1997)] approximate the power based on fixed alternatives. A more rigorous and potentially more accurate approach, ...