Bayesian model averaging in time-series studies of air pollution and mortality.
The issue of model selection in time-series studies assessing the acute health effects from short-term exposure to ambient air pollutants has received increased scrutiny in the past 5 yr. Recently, Bayesian model averaging (BMA) has been applied to allow for uncertainty about model form in assessing the association between mortality and ambient air pollution. While BMA has the potential to allow for such uncertainties in risk estimates, Bayesian approaches in general and BMA in particular are not panaceas for model selection., Since misapplication of Bayesian methods can lead to erroneous conclusions, model selection should be informed by substantive knowledge about the environmental health processes influencing the outcome. This paper examines recent attempts to use BMA in air pollution studies to illustrate the potential benefits and limitations of the method.
Thomas
Duncan C
DC
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089-9011, USA. dthomas@usc.edu
Jerrett
Michael
M
Kuenzli
Nino
N
Louis
Thomas A
TA
Dominici
Francesca
F
Zeger
Scott
S
Schwarz
Joel
J
Burnett
Richard T
RT
Krewski
Daniel
D
Bates
David
D
D000397:Air Pollution / Q000009:adverse effects*; D001499:Bayes Theorem; D004781:Environmental Exposure / Q000009:adverse effects; D006801:Humans; D008962:Models, Theoretical; D012680:Sensitivity and Specificity; D013997:Time Factors; D035501:Uncertainty