Wheeler Matthew W, Cortinas Jose, Aerts Marc, Gift Jeffery S, Davis J Allen
Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, RTP, NC, USA.
European Food Safety Authority.
Environmetrics. 2022 Aug;33(5). doi: 10.1002/env.2728. Epub 2022 May 14.
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.
在通过化学毒性实验估算基准剂量（BMD）时，美国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均推荐使用模型平均法。尽管已有大量关于使用二分反应进行模型平均BMD估算的研究，但针对使用连续反应进行BMD估算的研究较少。在这种情况下，对BMD进行模型平均会带来额外的问题，因为假设分布对许多BMD定义至关重要，并且当先验选择一种误差分布时，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种误差分布。因此，我们定义了一种针对分布模型的连续模型平均方法，并证明它优于单分布模型平均法。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反应数据。
