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Multilevel models address the hierarchical nature of data.
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We use multilevel models to address our research questions.
Strengths of the study include the large number of patients and GPs participating, the adjustment for confounding patient and GP factors, the correction for GP-level clustering with multilevel modeling, and addressing the potential bias and loss of precision arising from missing values using multiple imputations.
Multilevel models are best equipped to address questions relating to modification of a particular hospital [ 29].
Neighborhood and community social environmental variables, such as the poverty rate available from the U.S. Census, were also assigned to the residential address for inclusion as confounders in our multilevel models.
Although a number of studies have addressed the issue of what constitutes a sufficient sample size in multilevel models, a consensus has yet to develop [ 31– 31].
Multilevel models (Bryk and Raudenbush 1992; Singer and Willett 2003), particularly three level models, can be used to address our research questions.
Data were analyzed with linear multilevel models.
Therefore, we must consider multilevel models with area effects.
The hypotheses were tested using several multilevel models.
Multilevel models.
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