A comparison of bias-corrected empirical covariance estimators with generalized estimating equations in small-sample longitudinal study settings

2018 ◽  
Vol 37 (28) ◽  
pp. 4318-4329 ◽  
Author(s):  
Whitney P. Ford ◽  
Philip M. Westgate
Keyword(s):  
Longitudinal Study ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Small Sample ◽  
Empirical Covariance Estimators ◽  
Empirical Covariance ◽  
Generalized Estimating

scholarly journals Performance of the Beta-Binomial Model for Clustered Binary Responses: Comparison with Generalized Estimating Equations

2021 ◽  
Vol 19 (1) ◽  
pp. 2-25
Author(s):  
Seongah Im
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Small Sample ◽  
Binomial Model ◽  
Sample Sizes ◽  
Binary Responses ◽  
Large Sample ◽  
Generalized Estimating

This study examined performance of the beta-binomial model in comparison with GEE using clustered binary responses resulting in non-normal outcomes. Monte Carlo simulations were performed under varying intracluster correlations and sample sizes. The results showed that the beta-binomial model performed better for small sample, while GEE performed well under large sample.

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.

scholarly journals Internalizing and externalizing problems in immigrant boys and girls

2014 ◽  
Vol 39 (3) ◽  
pp. 242-254 ◽  
Author(s):  
Carmen Paalman ◽  
Lieke van Domburgh ◽  
Gonneke Stevens ◽  
Robert Vermeiren ◽  
Peter van de Ven ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Externalizing Problems ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Prevention And Intervention ◽  
Internalizing And Externalizing Problems ◽  
The Relationship ◽  
Internalizing And Externalizing ◽  
Over Time ◽  
Generalized Estimating

This longitudinal study explores differences between native Dutch and immigrant Moroccan adolescents in the relationship between internalizing and externalizing problems across time. By using generalized estimating equations (GEE), the strength and stability of associations between internalizing and externalizing problems in 159 Moroccan and 159 Dutch adolescents was studied over a period of 4 years. No differences in strength of co-occurring problems were found between Moroccan and Dutch adolescents. However, for Moroccan adolescents, associations between problems increased over time, whereas in Dutch adolescents, associations remained stable. The increase of co-occurring problems may be a result of undertreatment and increasing complexity of problems in Moroccans during adolescence. The results of this study imply that investigating processes leading to co-occurring problems in subgroups of adolescents, such as immigrant youths, is needed to optimize prevention and intervention efforts.

Small-sample performance of the robust score test and its modifications in generalized estimating equations

2005 ◽  
Vol 24 (22) ◽  
pp. 3479-3495 ◽  
Author(s):  
Xu Guo ◽  
Wei Pan ◽  
John E. Connett ◽  
Peter J. Hannan ◽  
Simone A. French
Keyword(s):  
Generalized Estimating Equations ◽  
Score Test ◽  
Estimating Equations ◽  
Small Sample ◽  
Generalized Estimating

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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