Piegorsch Walter W, An Lingling, Wickens Alissa A, West R Webster, Peña Edsel A, Wu Wensong
Interdisciplinary Program in Statistics, University of Arizona, Tucson, AZ, USA.
Environmetrics. 2013 May 1;24(3):143-157. doi: 10.1002/env.2201.
An important objective in environmental risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a pre-specified Benchmark Response (BMR) in a dose-response experiment. In such settings, representations of the risk are traditionally based on a specified parametric model. It is a well-known concern, however, that existing parametric estimation techniques are sensitive to the form employed for modeling the dose response. If the chosen parametric model is in fact misspecified, this can lead to inaccurate low-dose inferences. Indeed, avoiding the impact of model selection was one early motivating issue behind development of the BMD technology. Here, we apply a frequentist model averaging approach for estimating benchmark doses, based on information-theoretic weights. We explore how the strategy can be used to build one-sided lower confidence limits on the BMD, and we study the confidence limits' small-sample properties via a simulation study. An example from environmental carcinogenicity testing illustrates the calculations. It is seen that application of this information-theoretic, model averaging methodology to benchmark analysis can improve environmental health planning and risk regulation when dealing with low-level exposures to hazardous agents.
环境风险评估的一个重要目标是估计最低暴露水平，即基准剂量（BMDs），它在剂量反应实验中会引发预先指定的基准反应（BMR）。在这种情况下，风险的表示传统上基于特定的参数模型。然而，一个众所周知的问题是，现有的参数估计技术对用于模拟剂量反应的形式很敏感。如果所选的参数模型实际上指定错误，这可能导致低剂量推断不准确。事实上，避免模型选择的影响是BMD技术发展背后的一个早期动机问题。在这里，我们基于信息论权重应用一种频率主义模型平均方法来估计基准剂量。我们探索如何使用该策略构建BMD的单侧下限置信限，并通过模拟研究来研究置信限的小样本性质。一个环境致癌性测试的例子说明了计算过程。可以看出，将这种信息论的模型平均方法应用于基准分析可以在处理低水平接触有害剂时改善环境卫生规划和风险监管。
