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Concerning the estimating procedures, the GEE method for marginal models is not difficult to implement and is now available in the major statistical analysis packages.

The interpretation of the odds ratio for patient-level risk factors (eβ) in ordinary logistic and marginal models is the same, but differs from the interpretation in the multilevel logistic model [ 29].

Results of crude and adjusted marginal models are shown in Table 3.

Marginal models are appropriate when inferences about the population average are the focus.

Therefore, marginal models were estimated based on generalized estimating equations (GEE).

Models with random effects are useful for patient level inference just as marginal models are useful for population level inference.

Neuhaus has suggested that marginal models are preferable for testing the effects of cluster-level covariates [ 17].

The parameters of the marginal models were estimated by the Generalized Estimating Equation (GEE) approach, using exchangeable working correlation matrices.

On a practical level, marginal models are also computationally simpler than mixed models, which is relevant when simulating the analysis thousands of times.

Marginal models are easy to implement and represent a first solution, but the random models, although more complex, use all available data and are more suitable for explicative studies.

Marginal models are often fitted using the GEE methodology, whereby the relationship between the response and covariates is modeled separately from the correlation between repeated measurements on the same individual [ 2].