Abstract

Toxicologists are often concerned with determining the dosage to which an individual can be exposed with an acceptable risk of adverse effect. These types of studies have been conducted widely in the past, and many novel approaches have been developed. Parametric techniques utilizing ANOVA and non-linear regression models are well represented in the literature. The biggest drawback of parametric approaches is the need to specify the adequate model. Recently, there has been an interest in nonparametric approaches to tolerable dosage estimation. In this work, we focus on the monotonically decreasing dose-response model where the response is a percent to control. This imposes two constraints on the nonparametric approach: the dose-response function must be monotonic and always positive. Here, we propose a Bayesian solution to this problem using a novel class of non-parametric models. A set of basis functions developed in this research is Alamri Monotonic spline (AM-spline). Our approach is illustrated using two simulated datasets and two experimental datasets from pesticide related research at the US Environmental Protection Agency.

摘要

毒理学家经常关注确定个体在可接受的不良反应风险下可以接触的剂量。这些类型的研究过去已经广泛进行，并且已经开发了许多新方法。利用 ANOVA 和非线性回归模型的参数化技术在文献中得到了很好的体现。参数化方法的最大缺点是需要指定合适的模型。最近，人们对可耐受剂量估计的非参数方法产生了兴趣。在这项工作中，我们专注于单调递减的剂量反应模型，其中反应是控制的百分比。这对非参数方法施加了两个限制：剂量反应函数必须是单调的并且总是正的。这里，我们使用一类新的非参数模型为这个问题提出了贝叶斯解决方案。本研究开发的一组基函数是 Alamri Monotonic spline (AM-spline)。我们的方法使用来自美国环境保护署农药相关研究的两个模拟数据集和两个实验数据集进行说明。