Piegorsch Walter W, Xiong Hui, Bhattacharya Rabi N, Lin Lizhen
Program in Statistics, University of Arizona, Tucson, AZ, USA.
BIO5 Institute, University of Arizona, Tucson, AZ, USA.
Risk Anal. 2014 Jan;34(1):135-51. doi: 10.1111/risa.12066. Epub 2013 May 17.
Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose-response modeling. It is a well-known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low-dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal-response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap-based confidence limits for the BMD. We explore the confidence limits' small-sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty.
在定量风险评估中，传统上基准剂量（BMD）的估计是基于参数剂量反应模型。然而，一个众所周知的问题是，如果所选的参数模型不确定和/或设定错误，可能会导致不准确且可能不安全的低剂量推断。我们描述了一种基于等渗回归方法，利用定性反应数据估计BMD的非参数方法，并研究了基于非参数自举法的BMD相应置信限的使用。我们通过模拟研究探索了置信限的小样本性质，并用癌症风险评估的一个例子说明了计算过程。可以看出，当面临参数模型不确定性问题时，这种非参数方法可为BMD估计提供一种有用的替代方法。
