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Regression models were then estimated, particularly Generalized Estimated Equations (GEE), in order to generate population-averaged estimates.
Finnally, we performed Generalized Estimating Equations analyses (GEE) with exchangeable working correlation and robust variance estimation.
Generalized estimating equations.
Generalized estimating equations were used to account for sibling clustering.
Generalized Estimating Equations (GEE) were used for multivariate analysis.
Generalized estimating equations were used to compare mean responses, with p ≤ 0.05 considered significant.
Generalized Estimating Equations (GEE) were suitable to analyze the regression of the panel data.
Generalized estimating equations were used to perform logistic regression to analyze data.
Generalized estimating equations were used to accommodate nonindependence within randomized clusters of patients within clinic sites.
Generalized estimating equations were used to account for the clustering of respondents in census tracts.
Generalized estimating equations were used to estimate if demographic or clinical variables were associated with regularity.
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