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A comparison of Bayesian hierarchical modeling with group‐based exposure assessment in occupational epidemiology
Author(s) -
Xing Li,
Burstyn Igor,
Richardson David B.,
Gustafson Paul
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5791
Subject(s) - bayesian probability , logistic regression , computer science , epidemiology , bayesian hierarchical modeling , hierarchical database model , cohort , statistics , bayesian inference , data mining , econometrics , medicine , machine learning , artificial intelligence , mathematics , pathology
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.