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

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  • Giulia Roli
  • Paola Monari

Abstract

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).

Suggested Citation

  • Giulia Roli & Paola Monari, 2015. "Hierarchical Bayesian Models for the Estimation of Correlated Effects in Multilevel Data: A Simulation Study to Assess Model Performance," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(12), pages 2644-2653, June.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:12:p:2644-2653
    DOI: 10.1080/03610926.2013.806662
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