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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.

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].

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

Odds ratios for hospital risk factors in marginal models are interpreted as the odds of mortality for hospitals with one value of the factor compared to hospitals with another value of the factor.

Generalized estimating equation marginal models were used to evaluate the cross-sectional and longitudinal relations of HRT and modified MMSE score.

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

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

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].