Bayesian Model Averaging in Benchmark Dose Analysis

• Activity Number: 72
• Type: Topic Contributed
• Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Cuixian Chen*+ and Susan Simmons and Xiaosong Li and Yishi Wang and Walter Piegorsch and Qijun Fang
• Companies: The University of North Carolina Wilmington and The University of North Carolina Wilmington and The University of North Carolina Wilmington and The University of North Carolina Wilmington and University of Arizona and University of Arizona
• Keywords: Bayesian model averaging ; benchmark dose ; Markov chain Monte Carlo ; dose-response modelling ; model selection ; risk analysis

Abstract

In risk analysis of toxic agents, benchmark dose estimation has proven to be a valuable quantity and is commonly used by federal agencies in health risk assessment. However, one drawback of the benchmark dose estimator is that it is dependent on the assumed underlying risk model. Even when two risk models fit the data equally well, they can produce very different benchmark dose estimators for a given risk level. Herein, we propose a Bayesian model averaging methodology that overcomes this deficiency and does not require any model selection. We illustrate the usefulness of this approach in a simulation study and apply it to a data set from the National Toxicology Program.