When estimating a benchmark dose (BMD) from chemical toxicity experiments, model averaging is recommended by the National Institute for Occupational Safety and Health, World Health Organization and European Food Safety Authority. Though numerous studies exist for model average BMD estimation using dichotomous responses, fewer studies investigate it for BMD estimation using continuous response. In this setting, model averaging a BMD poses additional problems as the assumed distribution is essential to many BMD definitions, and distributional uncertainty is underestimated when one error distribution is chosen a priori. As model averaging combines full models, there is no reason one cannot include multiple error distributions. Consequently, we define a continuous model averaging approach over distributional models and show that it is superior to single distribution model averaging. To show the superiority of the approach, we apply the method to simulated and experimental response data.

中文翻译：

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用于基准剂量分析的连续模型平均：对分布形式进行平均


在根据化学毒性实验估算基准剂量 (BMD) 时，国家职业安全与健康研究所、世界卫生组织和欧洲食品安全局建议使用模型平均。尽管存在大量使用二分响应估计模型平均 BMD 的研究，但很少有研究调查使用连续响应估计 BMD 的情况。在这种情况下，平均 BMD 的模型会带来额外的问题，因为假设的分布对于许多 BMD 定义至关重要，并且当先验选择一个误差分布时，分布不确定性会被低估。由于模型平均结合了完整模型，因此没有理由不能包含多个误差分布。因此，我们定义了分布模型的连续模型平均方法，并表明它优于单一分布模型平均。为了展示该方法的优越性，我们将该方法应用于模拟和实验响应数据。