LOW DOSE RISK BOUNDS VIA SIMULTANEOUS CONFIDENCE BANDS

描述(改编自申请人的摘要)：量化环境和 暴露在有害物质中的其他有害风险是一个重要的 在风险评估和评估过程中的组成部分。的主要目标是 该项目是扩展和研究统计置信带，以用于 低剂量风险外推。应用程序针对环境和 使用人类或动物数据进行职业风险评估研究 基准或其他安全的低剂量有害物质，但在哪里进行研究 信息仅限于该试剂的高剂量水平。考虑使用的方法 用于估计不同端点的预测风险的置信度上限 在连续和离散尺度上进行测量。从同时开始 基准剂量(BMD)的置信限和下限 与特定风险相关联的风险进行计算。的一个重要特点是 同时构造是指任何基于倒置 同时置信限自动应用于 骨密度。 该方法将先前关于同时置信度范围的结果扩展到 具有剂量依赖方差模型的正态和非正态分布数据， 以及与非线性相关的高维参数空间 剂量-反应函数。该项目的评估阶段研究 基于蒙特卡罗的新方法的小样本运行特性 计算机计算，并将新方法应用于现有数据 定量风险评估中遇到的终点数/低剂量 外推问题。新方法填补了低剂量风险方面的现有空白。 外推，并可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.