Comparing Regression Coefficients Between Nested Linear Models for Clustered Data With Generalized Estimating Equations

2013 ◽  
Vol 38 (2) ◽  
pp. 172-189 ◽  
Author(s):  
Jun Yan ◽  
Robert H. Aseltine ◽  
Ofer Harel
Keyword(s):  
Generalized Estimating Equations ◽  
Linear Models ◽  
Clustered Data ◽  
Estimating Equations ◽  
Regression Coefficients ◽  
Generalized Estimating

Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations

2021 ◽  
pp. 107699862110174
Author(s):  
Francis L. Huang
Keyword(s):  
Least Squares ◽  
Generalized Estimating Equations ◽  
Multilevel Models ◽  
Clustered Data ◽  
Estimating Equations ◽  
Worked Examples ◽  
Ordinary Least Squares ◽  
Binary Outcomes ◽  
Similarities And Differences ◽  
Generalized Estimating

The presence of clustered data is common in the sociobehavioral sciences. One approach that specifically deals with clustered data but has seen little use in education is the generalized estimating equations (GEEs) approach. We provide a background on GEEs, discuss why it is appropriate for the analysis of clustered data, and provide worked examples using both continuous and binary outcomes. Comparisons are made between GEEs, multilevel models, and ordinary least squares results to highlight similarities and differences between the approaches. Detailed walkthroughs are provided using both R and SPSS Version 26.

scholarly journals Assessment of a Modified Sandwich Estimator for Generalized Estimating Equations with Application to Opioid Poisoning in MIMIC-IV ICU Patients

Stats ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 650-664
Author(s):  
Paul Rogers ◽  
Julie Stoner
Keyword(s):  
Repeated Measures ◽  
Generalized Estimating Equations ◽  
Rare Event ◽  
Estimating Equations ◽  
Small Sample ◽  
Repeated Measurements ◽  
Regression Coefficients ◽  
Binary Outcomes ◽  
Sandwich Estimator ◽  
Generalized Estimating

Longitudinal data is encountered frequently in many healthcare research areas to include the critical care environment. Repeated measures from the same subject are expected to correlate with each other. Models with binary outcomes are commonly used in this setting. Regression models for correlated binary outcomes are frequently fit using generalized estimating equations (GEE). The Liang and Zeger sandwich estimator is often used in GEE to produce unbiased standard error estimation for regression coefficients in large sample settings, even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large with few repeated measurements. The sandwich estimator’s asymptotic properties do not hold in small sample and rare-event settings. Under these conditions, the sandwich estimator underestimates the variances and is biased downwards. Here, the performance of a modified sandwich estimator is compared to the traditional Liang-Zeger estimator and alternative forms proposed by authors Morel, Pan, and Mancl-DeRouen. Each estimator’s performance was assessed with 95% coverage probabilities for the regression coefficients using simulated data under various combinations of sample sizes and outcome prevalence values with independence and autoregressive correlation structures. This research was motivated by investigations involving rare-event outcomes in intensive care unit settings.

An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals

2004 ◽  
Vol 29 (4) ◽  
pp. 421-437 ◽  
Author(s):  
Paolo Ghisletta ◽  
Dario Spini
Keyword(s):  
Social Sciences ◽  
Longitudinal Study ◽  
Generalized Estimating Equations ◽  
Oldest Old ◽  
Estimating Equations ◽  
Correlated Data ◽  
Regression Coefficients ◽  
Unbiased Estimation ◽  
The Social ◽  
Generalized Estimating

Correlated data are very common in the social sciences. Most common applications include longitudinal and hierarchically organized (or clustered) data. Generalized estimating equations (GEE) are a convenient and general approach to the analysis of several kinds of correlated data. The main advantage of GEE resides in the unbiased estimation of population-averaged regression coefficients despite possible misspecification of the correlation structure. This article aims to provide a concise, nonstatistical introduction to GEE. To illustrate the method, an analysis of selectivity effects in the Swiss Interdisciplinary Longitudinal Study on the Oldest Old is presented.

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