title: Bayesian model-averaged benchmark dose analysis via reparameterized quantal-response models.
abstract: 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.
authors.affiliation: Interdisciplinary Program in Statistics.
authors.forename: Q
authors.initials: Q
authors.lastname: Fang
authors.affiliation: Interdisciplinary Program in Statistics.|BIO5 Institute, University of Arizona, Tucson, AZ 85718.
authors.forename: W W
authors.initials: WW
authors.lastname: Piegorsch
authors.affiliation: Department of Mathematics and Statistics, University of North Carolina, Wilmington, NC 28403.
authors.forename: S J
authors.initials: SJ
authors.lastname: Simmons
authors.affiliation: Department of Mathematics and Statistics, University of North Carolina, Wilmington, NC 28403.
authors.forename: X
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Mathematics and Statistics, University of North Carolina, Wilmington, NC 28403.
authors.forename: C
authors.initials: C
authors.lastname: Chen
authors.affiliation: Department of Mathematics and Statistics, University of North Carolina, Wilmington, NC 28403.
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001555:Benzene Derivatives / Q000633:toxicity; D001699:Biometry / Q000379:methods; D015197:Carcinogenicity Tests / Q000706:statistics & numerical data; D004305:Dose-Response Relationship, Drug; D006801:Humans; D008452:Maximum Allowable Concentration; D015233:Models, Statistical; D018570:Risk Assessment / Q000706:statistics & numerical data; D035501:Uncertainty
chemical_list: D001555:Benzene Derivatives; C015763:cumene
keywords: Bayesian BMDL; Benchmark analysis; Dose-response analysis; Hierarchical modeling; Model uncertainty; Multimodel inference; Quantitative risk assessment