Bayesian BMD (BBMD) is a web-based benchmark dose (BMD) analysis system featuring the PyStan library for Markov Chain Monte Carlo simulation. The system is capable of using common dichotomous and continuous dose-response models to estimate the BMD for various individual dose-response models and model-averaged BMD.
The Principal Investigator (PI) of this project is Dr. Kan Shao who is responsible for the scientific methodology employed in the system and supervising the entire project. The lead software developer is Andy Shapiro.
Papers discussing this website and evaluation of methods are available:
- Bayesian benchmark dose: Shao K and Shapiro AJ. 2018. A Web-Based System for Bayesian Benchmark Dose Estimation. Environ Health Perspect. 126(1): 017002. doi: 10.1289/EHP1289.
- Probabilistic reference doses: Chiu et al. 2018. Beyond the RfD: Broad Application of a Probabilistic Approach to Improve Chemical Dose-Response Assessments for Noncancer Effects. Environ Health Perspect. 126(6): 067009. doi: 10.1289/EHP3368
Citing the website
Please cite our papers if you use the website for your own research. An preferred citation:
Shao K and Shapiro AJ. 2018. A Web-Based System for Bayesian Benchmark Dose Estimation. Environ Health Perspect. 126(1): 017002. doi: 10.1289/EHP1289.
For questions or suggestions, please contact:Kan Shao
Phone: (812) 856-2725
The project is currently supported by the Indiana University School of Public Health Developmental Grant for Pre-Tenure Faculty.