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A comparison of Bayesian hierarchical modeling with group‐based exposure assessment in occupational epidemiology
Journal article   Peer reviewed

A comparison of Bayesian hierarchical modeling with group‐based exposure assessment in occupational epidemiology

Li Xing, Igor Burstyn, David B Richardson and Paul Gustafson
Statistics in medicine, v 32(21), pp 3686-3699
20 Sep 2013
DOI: https://doi.org/10.1002/sim.5791
PMID: 23553785
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

MCMC group‐based exposure assessment Bayesian hierarchical model measurement error missing data
We build a Bayesian hierarchical model for relating disease to a potentially harmful exposure, by using data from studies in occupational epidemiology, and compare our method with the traditional group‐based exposure assessment method through simulation studies, a real data application, and theoretical calculation. We focus on cohort studies where a logistic disease model is appropriate and where group means can be treated as fixed effects. The results show a variety of advantages of the fully Bayesian approach and provide recommendations on situations where the traditional group‐based exposure assessment method may not be suitable to use. Copyright © 2013 John Wiley & Sons, Ltd.

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5 citations in Web of Science
6 citations in Scopus

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UN Sustainable Development Goals (SDGs)

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#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Mathematical & Computational Biology
Medical Informatics
Medicine, Research & Experimental
Public, Environmental & Occupational Health
Statistics & Probability
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