发表机构
Human Technopole; Department of Statistical Science, University College London; The Hospital for Sick Children; University of Toronto（人类技术中心; 伦敦大学学院统计科学系; 多伦多儿童医院; 多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出了一种在观察性数据受混杂影响时计算样本信息期望值（EVSI）的方法，通过逆目标试验模拟和逆概率加权调整，并应用于健康经济模型，发现EVSI低于随机化数据且损失随混杂水平增加。
AI 中文摘要
背景：样本信息期望值（EVSI）量化了收集额外证据以指导健康经济模型的价值。现有的EVSI方法通常假设理想化的数据收集机制，最常见的是随机对照试验（RCT）。然而，在许多现实情境中，额外证据来自观察性研究，这些研究可能受到混杂或其他偏倚的影响。在本工作中，我们定义了一种在额外数据为观察性且受混杂影响时计算EVSI的方法。方法：首先，我们定义了一个基于模拟的框架，其中受混杂影响的观察性数据通过逆目标试验模拟（ITTE）生成，这是一种从初始水平数据或人群结构的先验信息出发，以受控混杂水平生成观察性数据的方法。然后，我们应用逆概率加权（IPW）来获得针对相应随机化估计目标的数据调整汇总统计量。EVSI最终通过基于回归的方法计算。此外，我们提出了一种计算高效的方法来确定在理想化随机设计下恢复可实现EVSI所需的样本量。结果：我们将该方法应用于两个健康经济模型：一个正态-正态共轭模型和一个结合决策树与马尔可夫结构的化疗治疗模型。我们表明，即使经过调整，从观察性数据计算的EVSI也低于基于随机化数据的EVSI。EVSI的损失随着诱导混杂水平的增加而增加。结论：该方法将EVSI扩展到未来证据预期为观察性且受混杂影响的情况，使得在更现实和可行的数据收集场景中进行信息价值分析成为可能。当预期观察性证据时，它还为样本量规划提供了一种原则性方法。
英文摘要
Background: The Expected Value of Sample Information (EVSI) quantifies the value of collecting additional evidence to inform a health economic model. Existing EVSI methods typically assume idealized data collection mechanisms, most commonly randomized controlled trials (RCTs). However, in many realistic contexts, additional evidence comes from observational studies, which are subject to confounding or other bias. In this work, we define a methodology to calculate EVSI when additional data are observational and confounded. Methods: First, we define a simulation-based framework in which confounded observational data are generated through Inverse Target Trial Emulation (ITTE), a methodology that generates observational data with controlled levels of confounding starting from initial level data or prior information on population structure. Then, we apply inverse probability weighting (IPW) to obtain an adjusted summary statistic of the data targeting the corresponding randomized estimand. EVSI is finally computed using a regression based approach. Moreover, we propose a computationally efficient method to determine the sample size required to recover the EVSI achievable under an idealized randomized design. Results: We apply the methodology to two health economic models: a Normal Normal conjugate model and a chemotherapy treatment model combining a decision tree and Markov structure. We show that EVSI computed from observational data, even after adjustment, is lower than EVSI based on randomized data. The loss in EVSI increases with the level of induced confounding. Conclusions: This methodology extends EVSI when future evidence is expected to be observational and affected by confounding, enabling value-of-information analysis in more realistic and feasible data collection scenarios. It also provides a principled approach to sample size planning when observational evidence is anticipated.
