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首页> 外文期刊>Regulatory Toxicology and Pharmacology: RTP >Risk estimation for carcinogens based on epidemiological data: a structured approach, illustrated by an example on chromium.
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Risk estimation for carcinogens based on epidemiological data: a structured approach, illustrated by an example on chromium.

机译:基于流行病学数据的致癌物风险评估:一种结构化方法,以铬为例进行说明。

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摘要

It is generally recognized that human, epidemiological data, if available, are preferred as the starting point for quantitative risk analysis above the use of data from animal studies. Although methods to obtain proper risk estimates from epidemiological data are available, several impediments prevent their widespread application. These impediments include unfamiliarity with epidemiological methods and the lack of a structured and transparent approach. We described a framework to conduct quantitative cancer risk assessment based on epidemiological studies in a structured, transparent, and reproducible manner. Important features of the process include a weight-of-the-evidence approach, estimation of the optimal exposure-risk function by fitting a regression model to the epidemiological data, estimation of uncertainty introduced by potential biases and missing information in the epidemiological studies, and calculation of excess lifetime risk through a life table to take into account competing risks. Sensitivity analyses are a useful tool to obtain insight into the impact of assumptions made and the variability of the underlying data. The framework is sufficiently flexible to allow many types of data, ranging from published, sometimes incomplete data to detailed individual data, while maintaining an optimal result, i.e., a state-of-the-art risk estimate with confidence intervals, based on all available evidence of sufficient quality.
机译:一般认为,人类流行病学数据(如果有的话)优先于定量风险分析的起点,而不是使用动物研究的数据。尽管可以使用从流行病学数据中获取适当风险估计的方法,但仍有一些障碍阻止了它们的广泛应用。这些障碍包括不熟悉流行病学方法以及缺乏结构化和透明的方法。我们描述了一种基于流行病学研究以结构化,透明和可复制的方式进行定量癌症风险评估的框架。该过程的重要特征包括:证据权重法,通过对流行病学数据拟合回归模型来估计最佳暴露风险函数,估算由流行病学研究中潜在偏见和信息缺失所带来的不确定性,以及通过生命表计算额外的终生风险,以考虑竞争风险。敏感性分析是一种有用的工具,可帮助您深入了解所做假设的影响以及基础数据的可变性。该框架具有足够的灵活性,可以支持多种类型的数据,从已发布的数据(有时是不完整的数据到详细的个人数据),同时保持最佳结果,即基于所有可用数据的最新风险估计(具有置信区间)质量足够的证据。

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