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.

journal_name

Biometrics

journal_title

Biometrics

authors

Fang Q,Piegorsch WW,Simmons SJ,Li X,Chen C,Wang Y

doi

10.1111/biom.12340

subject

Has Abstract

pub_date

2015-12-01 00:00:00

pages

1168-75

issue

4

eissn

0006-341X

issn

1541-0420

journal_volume

71

pub_type

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