GSTDTAP  > 资源环境科学
DOI10.1289/EHP1289
A Web-Based System for Bayesian Benchmark Dose Estimation
Shao, Kan1; Shapiro, AndrewJ.2
2018
发表期刊ENVIRONMENTAL HEALTH PERSPECTIVES
ISSN0091-6765
EISSN1552-9924
出版年2018
卷号126期号:1
文章类型Article
语种英语
国家USA
英文摘要

BACKGROUND: 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 reliahle tool is needed to provide distributional estimates for BMD and other important quantities in dose response assessment.


OBJECTIVES: 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).


METHODS: 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. RESULTS: 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.


CONCLUSIONS: 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.


领域资源环境
收录类别SCI-E ; SSCI
WOS记录号WOS:000424212100008
WOS关键词CONTINUOUS END-POINTS ; RISK-ASSESSMENT ; FRAMEWORK ; PYTHON
WOS类目Environmental Sciences ; Public, Environmental & Occupational Health ; Toxicology
WOS研究方向Environmental Sciences & Ecology ; Public, Environmental & Occupational Health ; Toxicology
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/23158
专题资源环境科学
作者单位1.Indiana Univ, Sch Publ Hlth, Dept Environm & Occupat Hlth, 1025 E Seventh St, Bloomington, IN 47405 USA;
2.NIEHS, Natl Toxicol Program Div, NIH, Dept Hlth & Human Serv, POB 12233, Res Triangle Pk, NC 27709 USA
推荐引用方式
GB/T 7714
Shao, Kan,Shapiro, AndrewJ.. A Web-Based System for Bayesian Benchmark Dose Estimation[J]. ENVIRONMENTAL HEALTH PERSPECTIVES,2018,126(1).
APA Shao, Kan,&Shapiro, AndrewJ..(2018).A Web-Based System for Bayesian Benchmark Dose Estimation.ENVIRONMENTAL HEALTH PERSPECTIVES,126(1).
MLA Shao, Kan,et al."A Web-Based System for Bayesian Benchmark Dose Estimation".ENVIRONMENTAL HEALTH PERSPECTIVES 126.1(2018).
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Shao, Kan]的文章
[Shapiro, AndrewJ.]的文章
百度学术
百度学术中相似的文章
[Shao, Kan]的文章
[Shapiro, AndrewJ.]的文章
必应学术
必应学术中相似的文章
[Shao, Kan]的文章
[Shapiro, AndrewJ.]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。