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关注这篇论文的作者
James J. Chen
Ralph L. Kodell

致畸效应的定量风险评估

研究问题
如何利用动物窝内相关的致畸数据估计低剂量风险,并以基准剂量替代未观察到效应水平来确定安全剂量?
方法与思路
作者以 beta-binomial 分布处理窝效应,用 Weibull 模型描述剂量反应,再结合基准剂量、线性外推与似然比置信限。
材料陈述的贡献
摘要提出基于风险及置信要求的基准剂量程序,并以真实数据示范;作者认为该程序更有效利用实验数据并反映外推风险。
阅读关注点
应核对基准剂量的精确定义、窝效应模型与似然比区间的构造,特别关注数据范围之外的线性外推依据及敏感性。
阅读边界
材料为 OpenAlex 索引摘要,稿件版本未注明;未提供真实数据结果与模型检验,不能据此确认实际安全剂量,也不能推断刊本变化。

原文摘要

This article presents a quantitative procedure for using a “benchmark dose” to obtain low-dose risk estimates for reproductive and developmental toxic effects. This procedure combines the best features of the previously proposed methods for handling litter effects for teratology data and the currently used methods for quantitative risk assessment. The beta-binomial distribution is used to account for litter effects, and the Weibull dose—response model is used for modeling teratogenic effects. A benchmark dose, defined to be the lowest dose at which the excess risk does not exceed 1% with 95% confidence, is proposed to replace the no-observed-effect level (NOEL). The NOEL is generally the highest experimental dose that is not statistically different from the control; the NOEL approach does not use experimental data effectively for quantitative risk estimation. In this article, a lower limit on the safe dose is estimated by linearly extrapolating downward from the benchmark dose; this procedure is recommended because it is not strongly dependent on the model used in the data range. Linear extrapolation from a benchmark dose is essentially equivalent to the use of a safety factor in determining a safe dose from a NOEL. However, the linear extrapolation procedure reflects the risk at the estimated safe dose. Confidence limits on excess risks and safe doses are based on the beta-binomial likelihood ratio criterion rather than on the asymptotic distribution of the maximum likelihood estimates, because the latter have been shown to exhibit poor properties in the low-dose extrapolation problem. The proposed method is illustrated by application to a real data set.

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