评估价值与异质性的框架：以多癌早期检测测试的人群筛查早期模型为例
A framework for assessing value and heterogeneity, illustrated using an early model of population screening with a multi-cancer early detection test
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中文总结 AI 辅助
该研究提出了拆分价值与异质性的医疗干预价值评估框架，将其应用于英格兰MCED测试的人群筛查模型，发现Galleri筛查每人上限价值为0.135 QALYs，结肠/直肠等三类癌症贡献50%价值，结果稳健，可指导决策与研究优先级。
中文摘要 AI 辅助
引言：我们提出了一个通过拆分价值并考察异质性来评估医疗干预措施经济价值的框架，并将其应用于英格兰多癌早期检测（MCED）测试的人群筛查早期卫生经济模型中。这类技术的价值通常包括早期检测带来的获益，以及假阳性或过度诊断等潜在危害；价值也会在不同个体间存在差异，包括不同癌症类型与分期之间的差异。理解这些组成部分与异质性，对评估整体价值和优先开展未来研究至关重要。方法：我们调整了现有决策分析模型，以模拟无症状人群每年使用Galleri进行筛查的情况，从2024年英格兰国民保健服务（NHS）的视角，以质量调整生命年（QALYs）衡量结局。我们将上限价值（假设测试无成本）拆分为关键组成部分：癌症识别（诊断前与诊断后）、假阳性、过度诊断和分类错误；还按癌症类型进一步拆分诊断后价值，以探究异质性。结果：我们的分析预测，上限价值总体估计为每人0.135个QALYs，早期癌症识别是主要贡献因素，驱动因素是健康获益而非成本节约；结肠/直肠、肺和卵巢癌症是最大贡献者，占总价值的50%，这些结果在情景分析中保持稳健。结论：价值拆分可通过明确价值驱动因素、评估总体估计的合理性、探究异质性以及优先开展未来模型与证据开发活动，为决策提供指导。
英文摘要
Introduction: We present a framework to assess the economic value of healthcare interventions by disaggregating value and examining heterogeneity. We applied it to an early health-economic model of population screening in England with a multi-cancer early detection (MCED) test. Value for such technologies often includes benefits, such as those associated with earlier detection, alongside potential harms from, for example, false positives or overdiagnosis. Value also varies between individuals, including across cancer types and stages. Understanding these components and heterogeneity is crucial for assessing overall value and prioritising future research. Methods: We adapted an existing decision-analytic model to simulate annual Galleri screening in an asymptomatic population, measuring outcomes in Quality Adjusted Life Years (QALYs) from an English NHS perspective (year of 2024). We disaggregate headroom value (assuming no cost to the test) into key components: cancer identification (pre- and post-diagnosis), false positives, overdiagnosis, and misclassification. Post diagnosis value was further disaggregated by cancer type to explore heterogeneity. Results: Our analysis predicts an overall estimated headroom value of 0.135 QALYs per individual. Early cancer identification was a major contributor, driven by health gains rather than cost savings. Cancers of the colon/rectum, lung and ovary were the largest contributors, accounting for 50% of overall value. These results remained robust across scenario analyses. Conclusion: The value disaggregation can guide decision making by clarifying value drivers, assessing plausibility of overall estimates, exploring heterogeneity, and prioritising future model and evidence development activities.
