Department of Environmental and Occupational Health, School of Public Health, Indiana University, Bloomington, Indiana, USA.
National Toxicology Program Division, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA.
Environ Health Perspect. 2018 Jan 11;126(1):017002. doi: 10.1289/EHP1289.
Benchmark dose (BMD) modeling is an important step in human health risk assessment and is used as the default approach to identify the point of departure for risk assessment. A probabilistic framework for dose-response assessment has been proposed and advocated by various institutions and organizations; therefore, a reliable tool is needed to provide distributional estimates for BMD and other important quantities in dose-response assessment.
We developed an online system for Bayesian BMD (BBMD) estimation and compared results from this software with U.S. Environmental Protection Agency's (EPA's) Benchmark Dose Software (BMDS).
The system is built on a Bayesian framework featuring the application of Markov chain Monte Carlo (MCMC) sampling for model parameter estimation and BMD calculation, which makes the BBMD system fundamentally different from the currently prevailing BMD software packages. In addition to estimating the traditional BMDs for dichotomous and continuous data, the developed system is also capable of computing model-averaged BMD estimates.
A total of 518 dichotomous and 108 continuous data sets extracted from the U.S. EPA's Integrated Risk Information System (IRIS) database (and similar databases) were used as testing data to compare the estimates from the BBMD and BMDS programs. The results suggest that the BBMD system may outperform the BMDS program in a number of aspects, including fewer failed BMD and BMDL calculations and estimates.
The BBMD system is a useful alternative tool for estimating BMD with additional functionalities for BMD analysis based on most recent research. Most importantly, the BBMD has the potential to incorporate prior information to make dose-response modeling more reliable and can provide distributional estimates for important quantities in dose-response assessment, which greatly facilitates the current trend for probabilistic risk assessment. https://doi.org/10.1289/EHP1289.
基准剂量 (BMD) 建模是人类健康风险评估的重要步骤，被用作识别风险评估起点的默认方法。各种机构和组织已经提出并倡导了一种剂量反应评估的概率框架；因此，需要一种可靠的工具来提供 BMD 和剂量反应评估中其他重要量的分布估计。
我们开发了一个用于贝叶斯 BMD (BBMD) 估计的在线系统，并将该软件的结果与美国环保署的基准剂量软件 (BMDS) 进行了比较。
该系统建立在一个贝叶斯框架上，该框架的特点是应用马尔可夫链蒙特卡罗 (MCMC) 抽样进行模型参数估计和 BMD 计算，这使得 BBMD 系统从根本上不同于当前流行的 BMD 软件包。除了估计二项式和连续数据的传统 BMD 外，开发的系统还能够计算模型平均 BMD 估计值。
共使用了 518 个二项式和 108 个从美国环保署的综合风险信息系统 (IRIS) 数据库 (和类似数据库) 中提取的连续数据集作为测试数据，以比较 BBMD 和 BMDS 程序的估计值。结果表明，BBMD 系统在许多方面可能优于 BMDS 程序，包括更少的 BMD 和 BMDL 计算和估计失败。
BBMD 系统是一种有用的替代工具，用于估计 BMD，并具有基于最新研究的 BMD 分析的附加功能。最重要的是，BBMD 有可能纳入先验信息，使剂量反应建模更加可靠，并能够提供剂量反应评估中重要量的分布估计，这极大地促进了当前概率风险评估的趋势。https://doi.org/10.1289/EHP1289.
