The marginal models estimate population-averaged (or population-marginalized) parameters [ 30].
Marginal models via generalized estimating equations tested mediation effects.
In those instances, the use of marginal models or regression coefficient covariance matrix adjustment would also merit reconsideration.
While this feature poses no problem for marginal models, in which the mean and correlation functions are modeled directly and separately, the issue is more complicated in, for example, generalized linear mixed models.
In generalized regression models, several approaches can accommodate this lack of independence: (a) random effects; (b) fixed effects; (c) marginal models in the non-Gaussian case; and (d) regression coefficient covariance matrix adjustment.
Generalized estimating equation marginal models were used to evaluate the cross-sectional and longitudinal relations of HRT and modified MMSE score.
In addition to marginal models and mixed-effects models, this book covers topics on joint regression analysis based on Gaussian copulas and generalized state space models for longitudinal data from long time series.
I don't know if the solution will come from a new generation of entrepreneurs or from companies able to redefine themselves and grow from relatively marginal models, but I do know where it won't come from.
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).
While marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider non-linear models, let alone frame them within the above taxonomy.
Results of crude and adjusted marginal models are shown in Table 3.
