2004
DOI: 10.1177/1094428104263672
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Using Generalized Estimating Equations for Longitudinal Data Analysis

Abstract: The generalized estimating equation (GEE)

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Cited by 1,245 publications
(919 citation statements)
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References 36 publications
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“…Each of these methods offers particular advantages that depend on the nature of the data sample and the desired research focus (Hu et al, 1998). A particular advantage of GEEs is their ability to robustly estimate the regression coefficient's variances for data samples exhibiting high correlation between repeated measurements (Ballinger, 2004;Ghisletta & Spini 2004;Hu et al, 1998). This advantage led to the use of GEEs in this study, as the repeated measurements in the data sample are highly correlated between quarters.…”
Section: Methodsologymentioning
confidence: 99%
“…Each of these methods offers particular advantages that depend on the nature of the data sample and the desired research focus (Hu et al, 1998). A particular advantage of GEEs is their ability to robustly estimate the regression coefficient's variances for data samples exhibiting high correlation between repeated measurements (Ballinger, 2004;Ghisletta & Spini 2004;Hu et al, 1998). This advantage led to the use of GEEs in this study, as the repeated measurements in the data sample are highly correlated between quarters.…”
Section: Methodsologymentioning
confidence: 99%
“…This method of analysis allows the examination of the effects of each trial as well as the intragroup and intergroup differences over all the trials (45)(46)(47).…”
Section: Iugr and Feeding Impulsivitymentioning
confidence: 99%
“…To examine the association of institutional collectivism to overall initiation we conducted a repeated measures multiple regression using a generalized estimating equations (GEE) approach (Liang & Zeger, 1986) in a two-stage model (the main effects followed by interaction terms), with overall initiation treated as a continuous variable. GEE is an extension of the general linear model which allows the analysis of dependent variables with non-normal distributions and corrects the estimates for potential within-subject correlation of errors (within subject variability) between repeated decisions (Ballinger, 2004). As a participant's decision in one scenario may affect the decision in the other, each set of repeated observations should deal with autocorrelation as a result of the dependence between decisions.…”
Section: Resultsmentioning
confidence: 99%