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Bayesian hierarchical models in ecological studies of health–environment effects
Author(s) -
Richardson Sylvia,
Best Nicky
Publication year - 2003
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.571
Subject(s) - bayesian probability , computer science , markov chain monte carlo , hierarchy , hierarchical database model , probabilistic logic , random effects model , bayesian inference , bayesian hierarchical modeling , markov chain , variable order bayesian network , machine learning , multilevel model , econometrics , artificial intelligence , data mining , mathematics , meta analysis , medicine , economics , market economy
We describe Bayesian hierarchical models and illustrate their use in epidemiological studies of the effects of environment on health. The framework of Bayesian hierarchical models refers to a generic model building strategy in which unobserved quantities (e.g. statistical parameters, missing or mismeasured data, random effects, etc.) are organized into a small number of discrete levels with logically distinct and scientifically interpretable functions, and probabilistic relationships between them that capture inherent features of the data. It has proved to be successful for analysing many types of complex epidemiological and biomedical data. The general applicability of Bayesian hierarchical models has been enhanced by advances in computational algorithms, notably those belonging to the family of stochastic algorithms based on Markov chain Monte Carlo techniques. In this article, we review different types of design commonly used in studies of environment and health, give details on how to incorporate the hierarchical structure into the different components of the model (baseline risk, exposure) and discuss the model specification at the different levels of the hierarchy with particular attention to the problem of aggregation (ecological) bias. Copyright © 2003 John Wiley & Sons, Ltd.