Weighted multilevel models: A case study

B.T. West, L. Beer, G.W. Gremel, J. Weiser, C.H. Johnson, S. Garg and J. Skarbinski

American Journal of Public Health, 2015, vol. 105, issue 11, 2214-2215

Abstract: Recent advances in statistical software1 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.

Keywords: HIV Infections; human; multilevel analysis; organization and management; outpatient department; procedures; public health service; statistical model, Ambulatory Care Facilities; HIV Infections; Humans; Models, Statistical; Multilevel Analysis; Public Health Practice (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:aph:ajpbhl:10.2105/ajph.2015.302842_9

DOI: 10.2105/AJPH.2015.302842

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