Bayesian model averaging in time-series studies of air pollution and mortality

Duncan C. Thomas, Michael Jerrett, Nino Kuenzli, Thomas A. Louis, Francesca Dominici, Scott Zeger, Joel Schwarz, Richard T. Burnett, Daniel Krewski, David Bates

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)311-315
Number of pages5
JournalJournal of Toxicology and Environmental Health - Part A: Current Issues
Issue number3-4
StatePublished - Jan 2007

ASJC Scopus subject areas

  • Toxicology
  • Health, Toxicology and Mutagenesis


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