Using Prior Toxicological Data to Support Dose-Response Assessment─Identifying Plausible Prior Distributions for Dichotomous Dose-Response Models.

Using Prior Toxicological Data to Support Dose-Response Assessment─Identifying Plausible Prior Distributions for Dichotomous Dose-Response Models.
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DOI:
10.1021/acs.est.2c05872
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发表时间:
2022-11-15
影响因子:
11.4
通讯作者:
Chiu, Weihsueh
Chiu, Weihsueh
中科院分区:
环境科学与生态学1区
文献类型:
--
作者:
Shao, Kan;Ji, Chao;Chiu, Weihsueh

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基准剂量(BMD)方法学显著推进了剂量-反应分析的实践,并通过综合不同来源的剂量-反应信息,为提高BMD估计的可靠性创造了大量机会。特别是,通过贝叶斯框架中的先验分布整合现有的毒理学信息是一个有前途的,但没有得到充分研究的策略。研究目的是确定一种合理的方法,通过提供信息来纳入毒理学信息,然后使用二分数据支持BMD估计。在这项研究中有四个步骤:确定适当类型的分布参数在常见的剂量-反应模型;估计的参数确定的分布;调查的影响,替代策略的事先实施;并获得特定的终点先验研究如何事先引发的数据影响先验和BMD估计。使用通用数据库估计常见二分剂量-效应模型中每个参数的合理分布。实施信息先验的替代策略对BMD估计的影响有限,但使用信息先验可以显着降低BMD估计的不确定性。特定终点的信息先验与一般先验有很大不同,突出了对先验启发进行指导的必要性。该研究开发了一种实用的方法,利用信息先验,先进的贝叶斯BMD建模奠定了基础。
The benchmark dose (BMD) methodology has significantly advanced the practice of dose-response analysis and created substantial opportunities to enhance the plausibility of BMD estimation by synthesizing dose-response information from different sources. Particularly, integrating existing toxicological information via prior distribution in a Bayesian framework is a promising but not well-studied strategy. The study objective is to identify a plausible way to incorporate toxicological information through informative prior to support BMD estimation using dichotomous data. There are four steps in this study: determine appropriate types of distribution for parameters in common dose-response models; estimate the parameters of the determined distributions; investigate the impact of alternative strategies of prior implementation; and derive endpoint-specific priors to examine how prior-eliciting data affect priors and BMD estimates. A plausible distribution was estimated for each parameter in the common dichotomous dose-response models using a general database. Alternative strategies for implementing informative prior have limited impact on BMD estimation but using informative prior can significantly reduce uncertainty in BMD estimation. Endpoint-specific informative priors are substantially different from the general one, highlighting the necessity for guidance on prior elicitation. The study developed a practical way to employ informative prior and laid a foundation for advanced Bayesian BMD modeling.
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