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The OR for a risk factor in a marginal model is adjusted for the other risk factors included in Xijk.

The use of the marginal model is therefore questionable because an averaged effect is only poorly meaningful.

The marginal model is an extension of the linear regression model used with the OA Knee data.

GLMMs are most useful for making inferences about individuals and tracking individual trajectories, while the marginal model is more useful for inferences about population or sub-population averages.

For regression models with non-identity link such as beta regression, however, interpretation depends on whether a mixed model (i.e. a GLMM) or a marginal model is fitted.

The main difference to the marginal model is that a player cannot be at risk for the later injury until a prior event occurs.

A marginal model was fitted using Generalized Estimating Equations to model average weight trends for patients with good and poor outcome [20].

Longitudinal analysis with a pre-specified marginal model was fitted using Generalized Estimating Equations to compare weight trends for patients with good and poor outcome adjusting for potential confounders.

A propensity score weighted marginal model was thus fitted to compare groups for each outcome.

This characteristic is shown in figures 1 and 2, where the curves from the marginal model are flatter than the others.

In the case of only a random intercept, Nehaus et al [ 9] demonstrated that the estimates from the marginal model are systematically lower than those from the random model.