scholarly journals Model Selection of Generalized Estimating Equation With Divergent Model Size

2023 ◽  
Author(s):  
Shicheng Wu ◽  
Xin Gao ◽  
Raymond James Carroll
Keyword(s):  
Model Selection ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Model Size ◽  
Selection Of ◽  
Generalized Estimating

scholarly journals Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Wang
Keyword(s):  
Data Analysis ◽  
Estimating Equation ◽  
Marginal Model ◽  
Generalized Estimating Equation ◽  
Power Calculation ◽  
Recent Developments ◽  
Informative Cluster Size ◽  
Working Correlation ◽  
Selection Of ◽  
Generalized Estimating

Generalized Estimating Equation (GEE) is a marginal model popularly applied for longitudinal/clustered data analysis in clinical trials or biomedical studies. We provide a systematic review on GEE including basic concepts as well as several recent developments due to practical challenges in real applications. The topics including the selection of “working” correlation structure, sample size and power calculation, and the issue of informative cluster size are covered because these aspects play important roles in GEE utilization and its statistical inference. A brief summary and discussion of potential research interests regarding GEE are provided in the end.

Models for Longitudinal Data: A Generalized Estimating Equation Approach

Biometrics ◽  
1988 ◽  
Vol 44 (4) ◽  
pp. 1049 ◽  
Author(s):  
Scott L. Zeger ◽  
Kung-Yee Liang ◽  
Paul S. Albert
Keyword(s):  
Longitudinal Data ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Equation Approach ◽  
Generalized Estimating

scholarly journals Improved Generalized Estimating Equation Analysis via xtqls for Quasi–Least Squares in Stata

2007 ◽  
Vol 7 (2) ◽  
pp. 147-166 ◽  
Author(s):  
Justine Shults ◽  
Sarah J. Ratcliffe ◽  
Mary Leonard
Keyword(s):  
Least Squares ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Iterative Approach ◽  
Current Estimate ◽  
Cross Sectional ◽  
Equation Analysis ◽  
Correlation Structures ◽  
Correlation Parameters ◽  
Generalized Estimating

Quasi–least squares (QLS) is an alternative method for estimating the correlation parameters within the framework of the generalized estimating equation (gee) approach for analyzing correlated cross-sectional and longitudinal data. This article summarizes the development of qls that occurred in several reports and describes its use with the user-written program xtqls in Stata. Also, it demonstrates the following advantages of qls: (1) qls allows some correlation structures that have not yet been implemented in the framework of gee, (2) qls can be applied as an alternative to gee if the gee estimate is infeasible, and (3) qls uses the same estimating equation for estimation of β as gee; as a result, qls can involve programs already available for gee. In particular, xtqls calls the Stata program xtgee within an iterative approach that alternates between updating estimates of the correlation parameter α and then using xtgee to solve the gee for β at the current estimate of α. The benefit of this approach is that after xtqls, all the usual postregression estimation commands are readily available to the user.

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