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Biometrics. 2015 Dec;71(4):1168-75. doi: 10.1111/biom.12340. Epub 2015 Jun 23.

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

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Interdisciplinary Program in Statistics.
BIO5 Institute, University of Arizona, Tucson, AZ 85718.
Department of Mathematics and Statistics, University of North Carolina, Wilmington, NC 28403.


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.


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

[Indexed for MEDLINE]

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