Model-independent benchmark dose estimation for quantitative risk assessment

说明（由申请者提供）：量化接触危险物剂的有害风险是风险评价和分析过程中的一个重要组成部分。这一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新方法。适用于生物医学、职业、环境、毒理学或药理学研究，其中使用人类或动物数据来设定基准或其他安全的低剂量水平的危险制剂，但研究信息仅限于高剂量水平的制剂。由此产生的指导方针可以帮助改善公共卫生规划和风险监管时，处理低水平暴露于危险刺激。重点将是基于比例形式的风险/安全性数据，估计与预先选定的基准风险（BMR）水平相关的基准剂量（BMD）。这个探索性项目的具体重点是如何避免模型依赖性和模型选择偏差的问题，通过基于独立于模型的保序回归技术进行估计和推断。将建立无模型点估计量的理论渐近特征，并根据这些大样本基准剂量置信限（BMDLs）构建。为骨密度估计和计算置信限度而开发的软件将在互联网上分发，以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空白，模型独立的基准分析方法，并将应用于现代低剂量风险/安全评估的重要问题与比例数据。该项目的评价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该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估计范式。公共卫生相关性：本提案启动并探讨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水平的制剂时，确定基准或其他安全的危险刺激低剂量接触水平。常见于职业、环境、毒理学和药理学风险/安全性研究，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深入了解：（i）大多数现有的基准估计技术严重依赖于选择的刺激/剂量反应模型的形式;以及（ii）面对潜在的模型错误设定/模型选择偏差，现有的基准剂量估计统计策略可能会产生不准确，甚至可能不安全的低剂量推断。这项工作将探索制定强有力的基准剂量推断，由此产生的低剂量准则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。

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.