Bayesian model-averaged benchmark dose analysis via reparameterized quantal-response models
| Autor: | Cuixian Chen, Yishi Wang, Walter W. Piegorsch, Xiaosong Li, Susan J. Simmons, Qijun Fang |
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| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Estimation Bayes estimator General Immunology and Microbiology Computer science Applied Mathematics Bayesian probability General Medicine Target population Bayesian inference General Biochemistry Genetics and Molecular Biology Statistics Benchmark (computing) Point estimation General Agricultural and Biological Sciences Parametric statistics |
| Zdroj: | Biometrics. 71:1168-1175 |
| ISSN: | 0006-341X |
| DOI: | 10.1111/biom.12340 |
| Popis: | An important objective in biomedical risk assessment is estimation of minimum exposure levels that induce a pre-specified adverse response in a target population. The exposure/dose points in such settings are known as Benchmark Doses (BMDs). Recently, parametric Bayesian estimation for finding BMDs has become popular. A large variety of candidate dose-response models is available for applying these methods, however, leading to questions of model adequacy and uncertainty. Here we enhance the Bayesian estimation technique for BMD analysis by applying Bayesian model averaging to produce point estimates and (lower) credible bounds. We include reparameterizations of traditional dose-response models that allow for more-focused use of elicited prior information when building the Bayesian hierarchy. Performance of the method is evaluated via a short simulation study. An example from carcinogenicity testing illustrates the calculations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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