Model-independent benchmark dose estimation for quantitative risk assessment

描述(由申请人提供)：量化暴露于危险物质的有害风险是风险评估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新的统计推断方法。适用于生物医学、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危险制剂的基准或其他安全、低剂量水平，但研究信息仅限于高剂量水平的该制剂。由此产生的指导方针可以在处理低水平暴露于危险刺激时帮助改进公共卫生规划和风险监管。重点是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(BMR)水平相关的基准剂量(BMD)。在这个探索性项目中，特别关注的是如何通过基于与模型无关的保序回归技术进行估计和推断来避免模型依赖和模型选择偏差的问题。无模型点估计器的理论渐近特征将被建立，并将由这些大样本基准剂量置信限(BMDL)被构造。为估计骨密度和计算置信限而开发的软件将在互联网上分发，以使尽可能广泛的用户能够使用这些方法。该方法将填补与模型无关的基准分析方法的现有空白，并将应用于现代低剂量风险/安全性比例数据评估这一重要问题。该项目的评价阶段将把这些方法应用于从公共领域/现有文献收集的各种风险分析和量化反应数据，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点。这将评估新方法的操作能力，并确定基准分析方面的进一步进展是否可以基于这一新的估计范例。公共卫生相关性：这项建议发起并探索开发独立于模型的统计方法，当现有数据仅限于高剂量制剂时，用于设定危险刺激的基准或其他安全低剂量暴露水平。这些方法在职业、环境、毒理学和药理学风险/安全研究中很常见，它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洞察：(I)大多数现有的基准估计技术严重依赖于所选的形式来模拟刺激/剂量反应；(Ii)面对潜在的模型错误说明/模型选择偏差，现有的基准剂量估计的统计策略可能会得出不准确的、可能不安全的低剂量推断。这项工作将探索开发强有力的基准剂量推断，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进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.