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Weighted Multilevel Models: A Case Study
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DOI：10.2105/AJPH.2015.302842.png)
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Abstract
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Recent advances in statistical software(1) have enabled public health researchers to fit multilevel models to a variety of outcome variables. Multilevel models facilitate inferences regarding unexplained variability among randomly sampled clusters of units (e.g., hospitals) in outcomes of interest and identify covariates that explain the variance in a given outcome at each level of a particular data hierarchy (e.g., patients within hospitals).(2,3) Models with random intercepts enable researchers to accommodate correlations within higher-level units resulting from longitudinal or clustered study designs, and models with random coefficients enable researchers to identify higher-level covariates that explain between-cluster variance in relationships of interest.(2,3)
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