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In the last ten years the use of multilevel models has burgeoned in epidemiology.
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The multilevel models had two levels: measurement occasion (level 1) within individual (level 2).
Multiple membership multiple classification (MMMC) multilevel models have been around for some time (3) but are underused in social epidemiology.
Multilevel models have been used previously in studies examining associations between green environments and health [ 3, 4, 14] and physical activity [ 22].
Cross-classified multilevel models have allowed the fitting of models to account for the many environments in which children live and to measure associations between these different environments and adult health.
Ordinary and multilevel models have been used to investigate whether the choice of statistical methods affects which hospitals are classified as high- and low-performance outliers in the publicly available data at the New York State, Department of Health, Coronary Artery Bypass Graft (CABG) Surgery Report Card [ 25].
Multilevel modelling has already been employed in other physiological systems, mainly in the cardiovascular domain [ 44– 44].
The interpretation of hospital-level effects in multilevel model has been highlighted as problematic.
A third, multilevel modelling, has not yet migrated from academic to applied settings [ 12].
The multilevel model has been used to determine how much different components contribute to the variation in model results from the overall mean of 24% (Table 3).
A bivariate multilevel model has two outcomes, each with its own set of fixed effects relating it to the exposure and confounders.
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