Bayesian model averaging for benchmark dose estimation

Susan J. Simmons, Cuixian Chen, Xiaosong Li, Yishi Wang, Walter W Piegorsch, Qijun Fang, Bonnie Hu, G. Eddie Dunn

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Benchmark dose estimation is widely used in various regulatory and industrial settings to estimate acceptable exposure levels to hazardous or toxic agents by predefining a level of excess risk (US EPA in Benchmark dose technical guidance document. Technical Report #EPA/100/R-12/001. U.S. Environmental Protection Agency, Washington, DC, 2012). Although benchmark dose estimation is a popular method for identifying exposure levels of agents, there are some limitations and cautions on use of this methodology. One such concern is choice of the underlying risk model. Recently, advances have been made using Bayesian model averaging to improve benchmark dose estimation in the face of model uncertainty. Herein we employ the strategies of Bayesian model averaging to build model averaged estimates for the benchmark dose. The methodology is demonstrated via a simulation study and with real data.

Original languageEnglish (US)
Pages (from-to)5-16
Number of pages12
JournalEnvironmental and Ecological Statistics
Volume22
Issue number1
DOIs
StatePublished - 2015

Keywords

  • Bayesian model averaging
  • Benchmark dose estimation
  • Kernel smoothing
  • Posterior model probability

ASJC Scopus subject areas

  • Environmental Science(all)
  • Statistics, Probability and Uncertainty
  • Statistics and Probability

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