Hierarchical Bayesian Models for the Estimation of Correlated Effects in Multilevel Data: A Simulation Study to Assess Model Performance

In this article, we aim at assessing hierarchical Bayesian modeling for the analysis of multiple exposures and highly correlated effects in a multilevel setting. We exploit an artificial data set to apply our method and show the gains in the final estimates of the crucial parameters. As a motivating...

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 44; no. 12; pp. 2644 - 2653
Main Authors Roli, Giulia, Monari, Paola
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 18.06.2015
Taylor & Francis Ltd
Subjects
Bayesian analysis
Computer simulation
Correlated effects
Correlation analysis
Exposure
Hierarchical Bayesian modeling
Mathematical models
Multilevel
Multilevel data
Regression
Statistical analysis
Statistics
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Summary:In this article, we aim at assessing hierarchical Bayesian modeling for the analysis of multiple exposures and highly correlated effects in a multilevel setting. We exploit an artificial data set to apply our method and show the gains in the final estimates of the crucial parameters. As a motivating example to simulate data, we consider a real prospective cohort study designed to investigate the association of dietary exposures with the occurrence of colon-rectum cancer in a multilevel framework, where, e.g., individuals have been enrolled from different countries or cities. We rely on the presence of some additional information suitable to mediate the final effects of the exposures and to be arranged in a level-2 regression to model similarities among the parameters of interest (e.g., data on the nutrient compositions for each dietary item).
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ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2013.806662