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In general, similar results were obtained from a more parsimonious model for the clustering based on marginal models with robust standard errors (i.e., a generalized estimating equation framework).
Analysis was performed using marginal models with generalized estimating equations.
Similarly to GLS, GEE produces marginal models with correct SE estimates and inferences.
Relationships between the GRS and CAC, CarCP, AACP, history of CVD, and history of MI were examined using marginal models with incorporation of generalized estimating equations.
Relationships between the GRS and HDL, LDL, triglycerides, CAC, and prior history of CVD and MI were examined using marginal models with generalized estimating equations.
Associations between each GRS and a self-reported history of CVD, coronary artery calcified plaque (CAC) determined by noncontrast computed tomography scan, all-cause mortality, and CVD mortality were examined using marginal models with generalized estimating equations and Cox proportional hazards models.
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We performed longitudinal data analysis to evaluate the effect of time on EQ-Index adjusting for important covariates, building a marginal model with generalized estimating equations.
*Computed from unadjusted marginal model with random effect for centre.
The Marginal model, with non-weighted fit, displays a bias for smoking similar to the same model without losses.
One aim of this article is to compare the results of the marginal model with those from the conditional model.
We used a marginal model with the Early RA data and used GEEs to estimate the regression coefficients.
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