LOW DOSE RISK BOUNDS VIA SIMULTANEOUS CONFIDENCE BANDS

描述（改编自申请人的摘要）：量化环境和 暴露于有害物质的其他有害风险是一个重要的 在风险评估和评价过程中。的首要目标 这个项目是为了扩展和研究统计置信带， 低剂量风险外推法。适用于环境和 职业风险评估研究，其中使用人类或动物数据来确定 基准或其他安全的低剂量水平的危险剂，但如果研究 信息仅限于药剂的高剂量水平。方法被认为 用于估计各种终点预测风险的置信上限 在连续和离散尺度上测量。从同时 置信界限，基准剂量（BMD）的置信下限 与特定风险相关的风险。的重要特征 同时构造的一个重要特征是，任何基于颠倒 同时置信界限自动应用于 BMD。 该方法扩展了以前的结果，同时置信带， 正态和非正态分布数据与剂量依赖性方差模型， 以及与非线性相关的高维参数空间 剂量反应函数该项目的一个评估阶段研究了 小样本操作特性的新方法，通过蒙特卡罗 计算机计算，并将新方法应用于现有数据， 定量风险评估/低剂量中遇到的终点数量 外推问题新方法填补了低剂量风险的现有空白 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 applicant's abstract): Quantifying environmental and other detrimental risks from exposure to hazardous agents is an important component in the process of risk evaluation and assessment. The primary goal of this project is to extend and study statistical confidence bands for use in low-dose risk extrapolation. Application is directed to environmental and occupational risk assessment studies where human or animal data are used to set benchmark or other safe low-dose levels of a hazardous agent, but where study information is limited to high dose levels of the agent. Methods are considered for estimating upper confidence limits on predicted risk for various endpoints measured on both continuous and discrete scales. From the simultaneous confidence bounds, lower confidence limits on the benchmark dose (BMD) 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 BMD. The methodology extends previous results for simultaneous confidence bands to normally and non-normally distributed data with dose-dependent variance models, and to higher-dimensional parameter spaces associated with non-linear dose-response functions. 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 endpoints encountered in quantitative risk assessment/low-dose extrapolation problems. 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.