Abstract
This chapter extends the results in Chap. 9. It is common to come into contact with data that have a hierarchical or clustered structure. Examples include patients within a hospital, students within a class, factories within an industry, or families within a neighborhood. In such cases, there is variability between the clusters, as well as variability between the units which are nested within the clusters. Hierarchical models take into account the variability at each level of the hierarchy, and thus allow for the cluster effects at different levels to be analyzed within the models (The Annals of Thoracic Surgery 72(6):2155–2168, 2001). This chapter tells how one can use the information from different levels to produce a subject-specific model. This is a three-level nested design but can be expanded to higher levels, though readily available computing may be challenge.
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Wilson, J.R., Lorenz, K.A. (2015). Hierarchical Logistic Regression Models. In: Modeling Binary Correlated Responses using SAS, SPSS and R. ICSA Book Series in Statistics, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-23805-0_10
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