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首页> 外文期刊>Regulatory Toxicology and Pharmacology: RTP >An empirical comparison of low-dose extrapolation from points of departure (PoD) compared to extrapolations based upon methods that account for model uncertainty
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An empirical comparison of low-dose extrapolation from points of departure (PoD) compared to extrapolations based upon methods that account for model uncertainty

机译：从出发点（PoD）进行小剂量外推与基于模型不确定性方法的外推进行的经验比较

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摘要

Experiments with relatively high doses are often used to predict risks at appreciably lower doses. A point of departure (PoD) can be calculated as the dose associated with a specified moderate response level that is often in the range of experimental doses considered. A linear extrapolation to lower doses often follows. An alternative to the PoD method is to develop a model that accounts for the model uncertainty in the dose-response relationship and to use this model to estimate the risk at low doses. Two such approaches that account for model uncertainty are model averaging (MA) and semi-parametric methods. We use these methods, along with the PoD approach in the context of a large animal (40,000+ animal) bioassay that exhibited sub-linearity. When models are fit to high dose data and risks at low doses are predicted, the methods that account for model uncertainty produce dose estimates associated with an excess risk that are closer to the observed risk than the PoD linearization. This comparison provides empirical support to accompany previous simulation studies that suggest methods that incorporate model uncertainty provide viable, and arguably preferred, alternatives to linear extrapolation from a PoD.
机译：通常使用较高剂量的实验来预测较低剂量下的风险。出发点（PoD）可以计算为与指定的中等反应水平相关的剂量，该水平通常在所考虑的实验剂量范围内。通常会进行线性推断以降低剂量。 PoD方法的替代方法是开发一个模型，该模型考虑剂量反应关系中的模型不确定性，并使用该模型来估计低剂量时的风险。两种解决模型不确定性的方法是模型平均（MA）和半参数方法。我们在展示亚线性的大型动物（40,000多只动物）生物测定中使用这些方法以及PoD方法。当模型适合高剂量数据并预测低剂量风险时，考虑模型不确定性的方法所产生的剂量估计与与PoD线性化相比更接近观察到的风险的过量风险有关。这种比较为先前的仿真研究提供了经验支持，这些仿真研究表明，包含模型不确定性的方法为PoD的线性外推提供了可行且可以说是首选的替代方法。

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