Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD, USA.
Risk Anal. 2013 Sep;33(9):1608-19. doi: 10.1111/risa.12004. Epub 2013 Jan 22.
The article proposes and investigates the performance of two Bayesian nonparametric estimation procedures in the context of benchmark dose estimation in toxicological animal experiments. The methodology is illustrated using several existing animal dose-response data sets and is compared with traditional parametric methods available in standard benchmark dose estimation software (BMDS), as well as with a published model-averaging approach and a frequentist nonparametric approach. These comparisons together with simulation studies suggest that the nonparametric methods provide a lot of flexibility in terms of model fit and can be a very useful tool in benchmark dose estimation studies, especially when standard parametric models fail to fit to the data adequately.
本文提出并研究了两种贝叶斯非参数估计程序在毒理学动物实验基准剂量估计中的性能。该方法使用了多个现有的动物剂量-反应数据集进行说明，并与标准基准剂量估计软件（BMDS）中可用的传统参数方法以及已发表的模型平均方法和频率非参数方法进行了比较。这些比较以及模拟研究表明，非参数方法在模型拟合方面提供了很大的灵活性，并且在基准剂量估计研究中可能是一个非常有用的工具，特别是当标准参数模型不能充分拟合数据时。
