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在健康技术评估的治疗序列中,跨学科研究和真实世界数据面临的挑战与机遇。

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments.

机构信息

Sheffield Centre for Health and Related Research (SCHARR), Division of Population Health, School of Medicine and Population Health, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK.

Delta Hat Limited, Nottingham, UK.

出版信息

Pharmacoeconomics. 2024 May;42(5):487-506. doi: 10.1007/s40273-024-01363-1. Epub 2024 Apr 1.

Abstract

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

摘要

随着治疗选择的不断增加,治疗序列的评估在健康技术评估(HTA)中变得至关重要。本综述系统地探讨了评估序列所固有的多方面挑战,深入研究了它们的相互作用和细微差别,这些都超出了经济模型结构的范围。我们从关键方法学研究中综合了文献“路线图”,突出了最近进展和新兴研究主题的演变。将这些见解与 HTA 指南进行比较,以确定未来研究的潜在途径。我们的研究结果揭示了序列评估中存在的一系列挑战,包括选择适当的决策分析建模方法和对照物,在数据稀缺的情况下得出适当的临床有效性证据,仔细审查有效性假设和统计调整,考虑治疗替代,以及优化模型计算。整合来自不同学科的方法——统计学、流行病学、因果推断、运筹学和计算机科学——已证明在解决这些挑战方面具有潜力。有必要对应用研究进行更新综述,以详细了解这些方法在多大程度和何种方式上得到了实施。治疗序列有效性的数据稀缺性是一个主要关注点,特别是因为治疗序列在临床试验中很少进行比较。真实世界数据(RWD)为捕获有效性证据提供了一种替代方法,未来的研究应优先利用因果推断方法,特别是目标试验模拟,使用 RWD 评估治疗序列的有效性。这种方法也适用于分析包含测序信息的试验,并在从异质来源综合证据时调整间接比较。这些研究工作可以为 HTA 建议的审查提供支持,并有助于综合涉及治疗序列的外部对照臂。

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