Risk Sciences International, Ottawa, Canada.
School of Mathematics and Statistics, Carleton University, Ottawa, Canada.
Risk Anal. 2023 Mar;43(3):498-515. doi: 10.1111/risa.13931. Epub 2022 Apr 22.
A number of investigators have explored the use of value of information (VOI) analysis to evaluate alternative information collection procedures in diverse decision-making contexts. This paper presents an analytic framework for determining the value of toxicity information used in risk-based decision making. The framework is specifically designed to explore the trade-offs between cost, timeliness, and uncertainty reduction associated with different toxicity-testing methodologies. The use of the proposed framework is demonstrated by two illustrative applications which, although based on simplified assumptions, show the insights that can be obtained through the use of VOI analysis. Specifically, these results suggest that timeliness of information collection has a significant impact on estimates of the VOI of chemical toxicity tests, even in the presence of smaller reductions in uncertainty. The framework introduces the concept of the expected value of delayed sample information, as an extension to the usual expected value of sample information, to accommodate the reductions in value resulting from delayed decision making. Our analysis also suggests that lower cost and higher throughput testing also may be beneficial in terms of public health benefits by increasing the number of substances that can be evaluated within a given budget. When the relative value is expressed in terms of return-on-investment per testing strategy, the differences can be substantial.
许多研究人员已经探索了使用信息价值(Value of Information,VOI)分析来评估不同决策背景下替代信息收集程序的方法。本文提出了一个用于确定风险决策中使用的毒性信息价值的分析框架。该框架专门用于探索不同毒性测试方法之间与成本、及时性和不确定性减少相关的权衡。通过两个说明性应用程序演示了所提出框架的使用,尽管基于简化的假设,但这些应用程序展示了通过使用 VOI 分析可以获得的见解。具体而言,这些结果表明,信息收集的及时性对化学毒性测试的 VOI 估计有重大影响,即使不确定性的减少较小也是如此。该框架引入了延迟样本信息的预期价值的概念,作为通常样本信息的预期价值的扩展,以适应由于延迟决策而导致的价值降低。我们的分析还表明,更低的成本和更高的吞吐量测试也可能通过增加在给定预算内可以评估的物质数量而有益于公共健康效益。当相对价值以每个测试策略的投资回报率表示时,差异可能很大。