Abstract
An important objective in environmental risk assessment is the estimation of minimum exposure levels called benchmark doses (BMDs), which induce a pre‐specified benchmark response in a target population. Established inferential approaches for BMD analysis typically involve one‐sided, frequentist confidence limits, leading in practice to what are called benchmark dose lower Limits (BMDLs). Appeal to Bayesian modeling and credible limits for building BMDLs is far less developed, however. Indeed, for the few existing forms of Bayesian BMDs, informative prior information is seldom incorporated. We develop reparameterized quantal‐response models that explicitly describe the BMD as a target parameter. Our goal is to obtain an improved estimation and calculation archetype for the BMD and for the BMDL by quantifying prior beliefs representing parameter uncertainty in the statistical model. Implementation is facilitated via a Monte Carlo‐based adaptive Metropolis algorithm to approximate the posterior distribution. An example from environmental carcinogenicity testing illustrates the methodology. Copyright © 2015 John Wiley & Sons, Ltd.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.