Bayesian model-averaged benchmark dose analysis via reparameterized quantal-response models

Q. Fang, W. W. Piegorsch, S. J. Simmons, X. Li, C. Chen, Y. Wang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


An important objective in biomedical and environmental risk assessment is estimation of minimum exposure levels that induce a pre-specified adverse response in a target population. The exposure points in such settings are typically referred to as benchmark doses (BMDs). Parametric Bayesian estimation for finding BMDs has grown in popularity, and a large variety of candidate dose-response models is available for applying these methods. Each model can possess potentially different parametric interpretation(s), however. We present reparameterized dose-response models that allow for explicit use of prior information on the target parameter of interest, the BMD. We also enhance our Bayesian estimation technique for BMD analysis by applying Bayesian model averaging to produce point estimates and (lower) credible bounds, overcoming associated questions of model adequacy when multimodel uncertainty is present. An example from carcinogenicity testing illustrates the calculations.

Original languageEnglish (US)
Pages (from-to)1168-1175
Number of pages8
Issue number4
StatePublished - Dec 1 2015


  • Bayesian BMDL
  • Benchmark analysis
  • Dose-response analysis
  • Hierarchical modeling
  • Model uncertainty
  • Multimodel inference
  • Quantitative risk assessment

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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