Statistical Methods in Evidence-based Risk Assessment

该提案的主要研究主题是信息价值(VOI)分析。在初步工作中，已经开发了用于VOI分析的新的贝叶斯框架，允许在为支持风险评估而收集的数据的成本、不确定性和及时性之间进行权衡。这一新框架的一个重要组成部分是明确说明在获取关于重大人口健康风险的信息方面的延误成本，这可能导致重大的公共健康负担，等待查明并消除或减少这些风险。该框架考虑了两种风险决策范式：利益-风险决策者(BRDM)和目标-风险决策者(TRDM)，前者寻求最大限度地减少与所关注的风险问题相关的总社会成本(从经济角度看，对人口健康的不利影响)，后者试图确保风险低于预定水平。VOI框架在测试人类接触的大量化学品方面的初步应用表明，使用基于高通量体外筛选(HTS)测试的所谓新方法(NAMS)将比传统的基于动物的毒性测试提供更大的投资回报(ROI)，后者虽然受不确定性的影响较小，但进行起来要昂贵和耗时得多。作为这项提案的一部分，还将探讨另外两个研究主题。首先，将审查暴露-反应曲线上的新的出发点(Pod)，作为建立人体暴露指南的基础，特别是与生物信号与背景响应率不能从统计上区分的点相对应的信噪比交叉剂量(SNCD)。其次，将开发估算SNCD的最佳实验设计，以便获得最佳的人体暴露指南。总体而言，这项研究将通过应用VOI分析来确定支持人类健康风险评估的最具信息量的数据来源，通过使用更精细的暴露-反应分析来制定改进的人体暴露指南，并通过制定最佳实验设计来最大限度地获得关于暴露-反应曲线形状的统计信息，从而极大地加强循证风险评估做法。

The major research theme underlying the proposal is value-of-information (VOI) analysis. In preliminary work, a new Bayesian framework for VOI analysis has been developed, allowing trade-offs between the cost, uncertainty, and timeliness of data collected in support of risk assessment. An important component of this new framework is the explicit characterization of the cost of delay in acquiring information on critical population health risks, which can result in a significant public health burden, pending identification and elimination or reduction of those risks. The framework considers two risk decision making paradigms: a benefit-risk decision maker (BRDM), who seeks to minimize the total social cost associated with the risk issue of concern (with adverse population health impacts valued in economic terms), and a target-risk decision maker (TRDM), who seeks to ensure risk is below a pre-determined level. Initial applications of the VOI framework in testing large numbers of chemicals to which humans are exposed suggests that the use of so-called new approach methodologies (NAMs) based on high-throughput in vitro screening (HTS) tests will provide a greater return on investment (ROI) than traditional animal-based toxicity tests, which, although subject to less uncertainty, are much more expensive and time-consuming to conduct. Two additional research themes will also be pursued as part of this proposal. First, novel points of departure (PoD's) on the exposure-response curve will be examined as the basis for establishing human exposure guidelines, specifically the signal-to-noise crossover dose (SNCD) corresponding to the point where the biological signal cannot be statistically distinguished from the background response rate. Second, optimal experimental designs for estimating the SNCD will be developed in order to obtain the best possible human exposure guidelines. Collectively, this research will greatly strengthen evidence-based risk assessment practices through the application of VOI analyses to identify the most informative sources of data to support human health risk assessment; by developing improved human exposure guidelines through the use of more refined exposure-response analyses; and by developing optimal experimental designs to maximize the statistical information about the shape of the exposure-response curve.