健康技术评估中治疗序列的跨学科研究和真实世界数据的挑战与机遇。

IF 4.4 3区 医学 Q1 ECONOMICS PharmacoEconomics Pub Date : 2024-05-01 Epub Date: 2024-04-01 DOI:10.1007/s40273-024-01363-1
Jen-Yu Amy Chang, James B Chilcott, Nicholas R Latimer
{"title":"健康技术评估中治疗序列的跨学科研究和真实世界数据的挑战与机遇。","authors":"Jen-Yu Amy Chang, James B Chilcott, Nicholas R Latimer","doi":"10.1007/s40273-024-01363-1","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":" ","pages":"487-506"},"PeriodicalIF":4.4000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments.\",\"authors\":\"Jen-Yu Amy Chang, James B Chilcott, Nicholas R Latimer\",\"doi\":\"10.1007/s40273-024-01363-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":19807,\"journal\":{\"name\":\"PharmacoEconomics\",\"volume\":\" \",\"pages\":\"487-506\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PharmacoEconomics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s40273-024-01363-1\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/4/1 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PharmacoEconomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s40273-024-01363-1","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/4/1 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0

摘要

随着治疗方案的日益增多,治疗序列的评估已成为卫生技术评估(HTA)的关键。本综述系统地探讨了序列评估中固有的多方面挑战,深入探讨了它们之间的相互作用以及超越经济模型结构的细微差别。我们综合了主要方法学研究的文献 "路线图",强调了最新进展的演变和新出现的研究主题。我们将这些见解与 HTA 指南进行了比较,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价所面临的一系列挑战,包括选择适当的决策分析建模方法和比较对象、在数据稀缺的情况下获得适当的临床有效性证据、仔细审查有效性假设和统计调整、考虑治疗转移以及优化模型计算。整合不同学科的方法--统计学、流行病学、因果推断、运筹学和计算机科学--已显示出应对这些挑战的前景。有必要对应用研究进行最新审查,以便详细了解这些方法的实施范围和方式。治疗序列有效性方面的数据稀缺是一个主要问题,尤其是因为临床试验中很少对治疗序列进行比较。真实世界数据(RWD)为获取疗效证据提供了另一种方法,未来的研究应优先利用因果推理方法,特别是目标试验模拟法,使用 RWD 评估治疗序列的疗效。这种方法还可用于分析含有序列信息的试验,并在整理来自不同来源的证据时调整间接比较。此类研究工作可为 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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
PharmacoEconomics
PharmacoEconomics 医学-药学
CiteScore
8.10
自引率
9.10%
发文量
85
审稿时长
6-12 weeks
期刊介绍: PharmacoEconomics is the benchmark journal for peer-reviewed, authoritative and practical articles on the application of pharmacoeconomics and quality-of-life assessment to optimum drug therapy and health outcomes. An invaluable source of applied pharmacoeconomic original research and educational material for the healthcare decision maker. PharmacoEconomics is dedicated to the clear communication of complex pharmacoeconomic issues related to patient care and drug utilization. PharmacoEconomics offers a range of additional features designed to increase the visibility, readership and educational value of the journal’s content. Each article is accompanied by a Key Points summary, giving a time-efficient overview of the content to a wide readership. Articles may be accompanied by plain language summaries to assist readers who have some knowledge of, but not in-depth expertise in, the area to understand the scientific content and overall implications of the article.
期刊最新文献
Cost and Cost Effectiveness of Treatments for Psoriatic Arthritis: An Updated Systematic Literature Review. Effects and Costs of Hepatitis C Virus Elimination for the Whole Population in China: A Modelling Study. MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation-A Tutorial. Different Models, Same Results: Considerations When Choosing Between Approaches to Model Cost Effectiveness of Chimeric-Antigen Receptor T-Cell Therapy Versus Standard of Care. Evidence Following Conditional NICE Technology Appraisal Recommendations: A Critical Analysis of Methods, Quality and Risk of Bias.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1