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R-side, or marginal, random-effects models estimated using GLIMMIX provide conditional coefficient estimates for each individual, that is, estimates for individual animals that are conditional on the distribution of coefficient estimates across all individuals in the population.
They are usually classified into marginal or random-effects models.
Differences between the estimators of the marginal and random-effects parameters are expected (see above).
The marginal and random-effects models can also be used to identify other risk factors for disability.
This feature was already present, although not as visible, in the Marginal non-random loss to follow-up.
Marginal and random-effects models are probably the most common approaches for the analysis of correlated categorical response data.
First, the choice between the marginal and random-effects models depends mainly on the aims of the study.
This wide between-individual heterogeneity explains the differences between the parameter estimates of the marginal and random-effects models.
Carrière and Bouyer presented helpful strategies for choosing marginal and random-effects models in longitudinal binary responses.
In the models studied (marginal or random-effects model), the association between two successive responses is supposed to have the same structure through the whole survey.
Here, we present a strategy to model binary repeated responses and to explain how to choose between marginal and random-effects models.
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