Model-independent benchmark dose estimation for quantitative risk assessment

用于定量风险评估的独立于模型的基准剂量估计

• 批准号: 7648275
• 负责人: WALTER W PIEGORSCH
• 金额: $ 22.23万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7648275
• 关键词: Adopted; Affect; Animals; Area; Benchmarking; Binding; Biological Assay; Cells; Characteristics; Code; Computers; Data; Dependency; Development; Disease; Dose; Estimation Techniques; Evaluation; Exhibits; Exposure to; Face; Frequencies; Goals; Guidelines; Health Planning; Human; Internet; Isotonic Exercise; Lead; Literature; Malignant Neoplasms; Methodology; Methods; Modeling; Motivation; Mutation; Occupational; Operative Surgical Procedures; Outcome; Phase; Procedures; Process; Public Domains; Public Health; Regulation; Research; Research Project Grants; Risk; Risk Assessment; Safety; Sampling; Selection Bias; Series; Side; Site; Specific qualifier value; Statistical Methods; Stimulus; Survival Rate; System; Techniques; Tissues; Transgenic Animals; Work; animal data; base; improved; innovation; insight; model development; public health relevance; response; safety study; software development

DESCRIPTION (provided by applicant): Quantifying detrimental risks from exposure to hazardous agents is an important component in the process of risk evaluation and analysis. The focus of this exploratory project is to develop and study new methods of statistical inference for use in quantitative risk assessment. Application is directed to biomedical, occupational, environmental, toxicological, or pharmacological 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. The resulting guidelines can help improve public health planning and risk regulation when dealing with low-level exposures to hazardous stimuli. Emphasis will be on estimation of the benchmark dose (BMD) associated with a pre-selected level of benchmarked risk (BMR), based on risk/safety data in the form of proportions. Specific focus in this exploratory project centers on ways to avoid the problems of model-dependency and model selection bias, by basing estimation and inferences on model-independent isotonic regression techniques. The theoretical, asymptotic characteristics of the model-free point estimators will be established, and from these large-sample benchmark dose confidence limits (BMDLs) will be constructed. Software developed for BMD estimation and for calculating the confidence limits will be distributed on the Internet, to allow access to the methods by the widest possible corps of users. The methods will fill existing gaps in model-independent methods for benchmark analysis, and will have application to the important problem of modern low-dose risk/safety assessment with proportion data. An evaluation phase of the project will apply the methods to an assortment of risk analytic, quantal response data, assembled from the public domain/existing literature, and will study the small-sample operating characteristics of the methodology via Monte Carlo computer calculations. This will assess the operating capabilities of the new methods and determine if further advances in benchmark analysis can be based on this new estimation paradigm. PUBLIC HEALTH RELEVANCE: This proposal initiates and explores development of model-independent statistical approaches for setting benchmark or other safe low-dose exposure levels of a hazardous stimulus when existing data are limited to high-dose levels of the agent. Common in occupational, environmental, toxicological, and pharmacological risk/safety studies, the methods expand upon traditional benchmark risk analyses and provide insight into two scientifically pressing issues: (i) that most existing benchmark estimation techniques depend heavily on the forms chosen to model the stimulus/dose response; and (ii) that in the face of potential model misspecification/model selection bias, existing statistical strategies for benchmark dose estimation can yield inaccurate, and possibly unsafe, low-dose inferences. This work will explore development of robust benchmark dose inferences, from which resulting low-dose guidelines can improve public health planning and risk regulation when dealing with low-level exposures to hazardous stimuli.

描述（由申请人提供）：在风险评估和分析过程中，对接触有害物质的有害风险进行量化是一个重要组成部分。这个探索性项目的重点是开发和研究用于定量风险评估的统计推断的新方法。应用于生物医学、职业、环境、毒理学或药理学研究，在这些研究中，人类或动物数据被用来设定基准或其他安全、低剂量的危险物质水平，但研究信息仅限于该物质的高剂量水平。由此产生的指导方针有助于在处理低水平接触有害刺激时改善公共卫生规划和风险管理。重点将放在根据比例形式的风险/安全数据，估计与预先选定的基准风险水平相关的基准剂量（BMD）。这个探索性项目的重点是通过基于与模型无关的等渗回归技术的估计和推断来避免模型依赖和模型选择偏差的问题。将建立无模型点估计器的理论渐近特征，并从这些大样本基准剂量置信限（BMDLs）中构建。为弹道导弹密度估计和计算置信限而开发的软件将在互联网上分发，使尽可能多的用户能够使用这些方法。该方法将填补现有的模型无关基准分析方法的空白，并将应用于现代比例数据低剂量风险/安全评价的重要问题。该项目的一个评估阶段将把这些方法应用于从公共领域/现有文献中收集的各种风险分析、定量反应数据，并将通过蒙特卡罗计算机计算研究方法的小样本操作特征。这将评估新方法的操作能力，并确定基准分析的进一步进展是否可以基于这种新的评估范式。公共卫生相关性：本提案启动并探索了独立于模型的统计方法的发展，以便在现有数据仅限于高剂量剂量的情况下设定危险刺激的基准或其他安全低剂量暴露水平。在职业、环境、毒理学和药理学风险/安全研究中很常见，这些方法扩展了传统的基准风险分析，并提供了对两个科学紧迫问题的见解：(i)大多数现有的基准估计技术严重依赖于选择的形式来模拟刺激/剂量反应；（ii）面对潜在的模型规格错误/模型选择偏差，现有的基准剂量估计统计策略可能产生不准确的、可能不安全的低剂量推断。这项工作将探索制定可靠的基准剂量推断，由此产生的低剂量指南可以在处理低水平暴露于危险刺激时改善公共卫生规划和风险监管。