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Hierarchical modeling in spatial epidemiology

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This paper considers the basic concepts and methods used in hierarchical modeling for data arising in spatial epidemiology. Following discussion of basic statistical and epidemiological concepts relevant to small‐area health studies, the paper reviews the different approaches to model formulation, parameter estimation, and also software resources. WIREs Comput Stat 2014, 6:405–417. doi: 10.1002/wics.1315 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Applications of Computational Statistics > Health and Medical Data/Informatics Data: Types and Structure > Image and Spatial Data
Central Scotland: 26 census enumeration districts (EDs) in the center of the city of Falkirk. Respiratory cancer deaths [standardized mortality ratios (SMRs)] for the period 1976–1983. Scottish national rate used for standardization.
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Relative risk estimates from the uncorrelated heterogeneity (UH) model with no poverty covariate.
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Relative risk posterior mean estimates from the BYM convolution model with a poverty covariate included.
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Ohio county map: respiratory cancer standardized mortality ratios (SMRs) for four selected years: 1968, 1977, 1983, and 1988.
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South Carolina: congential deaths 1990 by county: standardized mortality ratio (SMR).
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Data: Types and Structure > Image and Spatial Data
Applications of Computational Statistics > Health and Medical Data/Informatics
Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory

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