LOW DOSE RISK BOUNDS VIA SIMULTANEOUS CONFIDENCE BANDS

描述：（改编自研究者摘要）本项目开发 并研究了可用于低剂量外推的统计置信界限 定量风险分析。 应用程序旨在应对这些风险 使用人类或动物数据设定基准的评估研究，或 其他安全的低剂量水平的毒性刺激，但研究信息 仅限于高剂量水平的刺激。 方法是为 估计预测风险和预测风险的置信上限 各种终点的额外风险，在连续和 离散尺度 从同时置信区间来看， 对与特定刺激相关的"有效"刺激或剂量（艾德）的限制 风险计算。 同时建设的一个重要特点 任何基于反转同时置信界限的推论 自动应用于ED上的反向边界。 该方法扩展了现有的同时预测带理论 这适用于数学预测方程， 风险评估/低剂量外推问题。 新概念开发于 为了实现这些目标， 剂量刻度的低剂量区域，以及对非正常 分布式数据和非线性，可能非单调的剂量反应 功能协调发展的 无限逆中同时界的推广 还考虑了低剂量外推的预测。 评价 该项目的第一阶段研究了小样本操作特性， 新的方法，通过蒙特卡罗计算机计算，并适用于 针对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突变频率 人类或动物/转基因系统中的光谱， 实验动物，或水生动物或其他动物的生殖能力限制 生态毒理学系统 新方法填补了低剂量 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.