Bayesian Model-Averaging Approach in Health Effects Studies: Sensitivity Analyses Using PM10 and Cardiopulmonary Hospital Admissions in Allegheny County, PA, and Simulated Data

• Activity Number: 382
• Type: Contributed
• Date/Time: Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
• Sponsor: Section on Risk Analysis
• Author(s): Ya-Hsiu Chuang*+ and Sati Mazumdar
• Companies: University of Pittsburgh and University of Pittsburgh
• Keywords: lagged effects ; Bayesian model averaging ; hyperparameters ; AIC ; BIC ; local Empirical Bayes

Abstract

Determining the lagged effects of ambient air levels of a pollutant on cardiac distress is important in health effect studies. Standard model selection procedures where a set of predictor variables is selected ignore the associated uncertainties and may lead to overestimation of effects. Bayesian model averaging approach takes account of model uncertainty by combining information from all possible models. Zellner's g-prior containing a hyperparameter g can account for model uncertainty and has potential usefulness in this endeavor. We conducted a sensitivity analysis for Bayesian model averaging with different calibrated hyperparameter g, viz., Akaike Information Criterion (AIC) prior, Bayes Information Criterion (BIC) prior, and Local Empirical Bayes estimate. Data from Allegheny County Air Pollution Study (ACAPS) and simulated data sets were used.