LOW DOSE RISK BOUNDS VIA SIMULTANEOUS CONFIDENCE BANDS

描述：(改编自调查人员摘要)这个项目开发 并研究了对低剂量外推有用的统计置信限 在定量风险分析中。应用程序旨在应对这些风险 使用人类或动物数据来设定基准或 其他安全的低剂量毒性刺激，但研究信息 仅限于高剂量的刺激。方法被派生为 估计预测风险和预测风险的置信度上限 不同终端的额外风险，在连续和 不连续的刻度。从同时的置信度范围来看，较低的置信度 对与特定刺激或剂量(ED)相关的“有效”刺激或剂量的限制 风险是经过计算的。同步施工的一个重要特点 任何基于倒置同时置信限的推论 自动应用于ED上的反边界。 该方法扩展了现有的同时预测波段理论 它适用于在定量计算中有用的数学预测方程 风险评估/低剂量外推问题。开发了新的概念 以实现这些目标，包括同时进行带宽优化 剂量刻度的低剂量区域以及在非正常情况下的应用 分布式数据和非线性的、可能的非单调剂量反应 功能。在无限逆中有用的联立界的推广 还考虑了对低剂量外推的预测。一项评估 该项目阶段研究了小样本操作的特点 新的方法通过蒙特卡罗计算机计算，并应用 一些低剂量风险终点的现有数据的新方法， 包括有害毒素或有毒代谢物的浓度水平 人类或动物受试者，实验动物体重减轻，突变 转基因动物和其他转基因系统中的频率，突变 人类或动物/转基因系统的光谱，致癌率 实验动物，或水生或其他繁殖能力限制 生态毒理系统。新方法填补了现有的低剂量的空白 风险外推，并应用于各种数据分析 定量风险评估中的情景。

DESCRIPTION: (Adapted from investigator's abstract) This project develops and studies statistical confidence bounds useful for low-dose extrapolation in quantitative risk analysis. Application is intended for those risk assessment studies where human or animal data are used to set benchmark or other safe low-dose levels of a toxic stimulus, but where study information is limited to high dose levels of the stimulus. Methods are derived for estimating upper confidence limits on predicted risk and on predicted additional risk for various endpoints, measured on both continuous and discrete scales. From the simultaneous confidence bounds, lower confidence limits on the "effective" stimulus or dose (ED) associated with a particular risk are calculated. An important feature of the simultaneous construction is that any inferences based on inverting the simultaneous confidence bounds apply automatically to inverse bounds on the ED. The methodology extends existing theory on simultaneous prediction bands which applies to mathematical prediction equations useful in quantitative risk assessment/low-dose extrapolation problems. New concepts developed in order to achieve these goals include simultaneous band optimization in low-dose regions of the dose scale, and application to non-normally distributed data and to non-linear, possibly non-monotone dose-response functions. Extensions to simultaneous bounds useful in unlimited inverse prediction for low-dose extrapolation also are considered. An evaluation phase of the project studies the small-sample operating characteristics of the new methodology via Monte Carlo computer calculations, and applies the new methods to existing data for a number of low-dose risk endpoints, including concentration levels of detrimental toxins or toxic metabolites in human or animal subjects, body weight losses in laboratory animals, mutation frequencies in transgenic animals and other transgenic systems, mutational spectra in human or animal/transgenic systems, carcinogenicity rates in laboratory animals, or reproductive capacity limitations in aquatic or other eco-toxicological systems. The new methods fill existing gaps in low-dose risk extrapolation, and have application to a wide variety of data-analytic scenarios in quantitative risk assessment.