Climate Change Data Portal
DOI | 10.1111/risa.12078 |
Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data | |
Shao, Kan; Gift, Jeffrey S. | |
发表日期 | 2014 |
ISSN | 0272-4332 |
卷号 | 34期号:1页码:101-120 |
英文摘要 | The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose-response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the hybrid method proposed by Crump, two strategies of BMA, including both maximum likelihood estimation based and Markov Chain Monte Carlo based methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose-response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose-response data. |
英文关键词 | Bayesian model averaging;benchmark dose;continuous data;model uncertainty |
语种 | 英语 |
WOS记录号 | WOS:000329544800006 |
来源期刊 | RISK ANALYSIS |
来源机构 | 美国环保署 |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/60692 |
作者单位 | US EPA, Natl Ctr Environm Assessment, Res Triangle Pk, NC 27711 USA |
推荐引用方式 GB/T 7714 | Shao, Kan,Gift, Jeffrey S.. Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data[J]. 美国环保署,2014,34(1):101-120. |
APA | Shao, Kan,&Gift, Jeffrey S..(2014).Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data.RISK ANALYSIS,34(1),101-120. |
MLA | Shao, Kan,et al."Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data".RISK ANALYSIS 34.1(2014):101-120. |
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 |
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 |
[Gift, Jeffrey S.]的文章 |
百度学术 |
百度学术中相似的文章 |
[Shao, Kan]的文章 |
[Gift, Jeffrey S.]的文章 |
必应学术 |
必应学术中相似的文章 |
[Shao, Kan]的文章 |
[Gift, Jeffrey S.]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
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。