• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

临床试验的适应性随机化

Adaptive randomization for clinical trials.

作者信息

Rosenberger William F, Sverdlov Oleksandr, Hu Feifang

机构信息

Department of Statistics, George Mason University, VA 22030, USA.

出版信息

J Biopharm Stat. 2012;22(4):719-36. doi: 10.1080/10543406.2012.676535.

DOI:10.1080/10543406.2012.676535
PMID:22651111
Abstract

In February 2010, the U.S. Food and Drug Administration (FDA, 2010 ) drafted guidance that discusses the statistical, clinical, and regulatory aspects of various adaptive designs for clinical trials. An important class of adaptive designs is adaptive randomization, which is considered very briefly in subsection VI.B of the guidance. The objective of this paper is to review several important new classes of adaptive randomization procedures and convey information on the recent developments in the literature on this topic. Much of this literature has been focused on the development of methodology to address past criticisms and concerns that have hindered the broader use of adaptive randomization. We conclude that adaptive randomization is a very broad area of experimental design that has important application in modern clinical trials.

摘要

2010年2月,美国食品药品监督管理局(FDA,2010)起草了一份指南,讨论了临床试验中各种适应性设计的统计学、临床和监管方面。适应性设计的一个重要类别是适应性随机化,该指南的第六部分B小节对其进行了简要讨论。本文的目的是回顾几类重要的新型适应性随机化程序,并传达有关该主题文献最新进展的信息。这些文献大多集中在方法学的开发上,以解决过去阻碍适应性随机化更广泛应用的批评和担忧。我们得出结论,适应性随机化是实验设计中一个非常广泛的领域,在现代临床试验中具有重要应用。

相似文献

1
Adaptive randomization for clinical trials.临床试验的适应性随机化
J Biopharm Stat. 2012;22(4):719-36. doi: 10.1080/10543406.2012.676535.
2
Adaptive designs for binary treatment responses in phase III clinical trials: controversies and progress.III期临床试验中二元治疗反应的适应性设计:争议与进展
Stat Methods Med Res. 2001 Oct;10(5):353-64. doi: 10.1177/096228020101000504.
3
Adaptive design of confirmatory trials: Advances and challenges.确证性试验的适应性设计:进展与挑战
Contemp Clin Trials. 2015 Nov;45(Pt A):93-102. doi: 10.1016/j.cct.2015.06.007. Epub 2015 Jun 14.
4
Understanding the FDA guidance on adaptive designs: historical, legal, and statistical perspectives.从历史、法律和统计学角度理解美国食品药品监督管理局关于适应性设计的指南。
J Biopharm Stat. 2010 Nov;20(6):1178-219. doi: 10.1080/10543406.2010.514462.
5
Balancing statistical and ethical considerations in planning clinical trials: recommendations for response-adaptive randomization urn designs.在规划临床试验时平衡统计和伦理考量:响应自适应随机化瓮设计的建议
J Biopharm Stat. 2018;28(6):1105-1118. doi: 10.1080/10543406.2018.1437172. Epub 2018 Feb 14.
6
Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group.通过对小群组的适应性随机化来实现剂量反应研究的最佳设计。
AAPS J. 2018 Jul 19;20(5):85. doi: 10.1208/s12248-018-0242-5.
7
Statistical inference for response adaptive randomization procedures with adjusted optimal allocation proportions.具有调整后最优分配比例的响应自适应随机化程序的统计推断。
J Biopharm Stat. 2017;27(5):732-740. doi: 10.1080/10543406.2016.1269780. Epub 2017 Jan 19.
8
The UPMC OPTIMISE-C19 (OPtimizing Treatment and Impact of Monoclonal antIbodieS through Evaluation for COVID-19) trial: a structured summary of a study protocol for an open-label, pragmatic, comparative effectiveness platform trial with response-adaptive randomization.UPMC OPTIMISE-C19(通过对COVID-19的评估优化单克隆抗体的治疗及影响)试验:一项开放标签、务实、具有反应适应性随机化的比较有效性平台试验的研究方案结构化总结。
Trials. 2021 May 25;22(1):363. doi: 10.1186/s13063-021-05316-3.
9
Randomization in clinical studies.临床研究中的随机化。
Korean J Anesthesiol. 2019 Jun;72(3):221-232. doi: 10.4097/kja.19049. Epub 2019 Apr 1.
10
Adaptive methods: telling "the rest of the story".自适应方法:讲述“故事的其余部分”。
J Biopharm Stat. 2010 Nov;20(6):1150-65. doi: 10.1080/10543406.2010.514457.

引用本文的文献

1
Adaptive designs were primarily used but inadequately reported in early phase drug trials.适应性设计主要用于早期药物试验中,但报告不充分。
BMC Med Res Methodol. 2024 Jun 5;24(1):130. doi: 10.1186/s12874-024-02256-9.
2
Mathematical programming tools for randomization purposes in small two-arm clinical trials: A case study with real data.用于小两臂临床试验随机化目的的数学规划工具:基于真实数据的案例研究。
Pharm Stat. 2024 Nov-Dec;23(6):794-812. doi: 10.1002/pst.2388. Epub 2024 Apr 13.
3
The Future Glioblastoma Clinical Trials Landscape: Early Phase 0, Window of Opportunity, and Adaptive Phase I-III Studies.
未来胶质母细胞瘤临床试验格局:早期 0 期、机会之窗和适应性 I-III 期研究。
Curr Oncol Rep. 2023 Sep;25(9):1047-1055. doi: 10.1007/s11912-023-01433-1. Epub 2023 Jul 4.
4
Response-adaptive randomization in clinical trials: from myths to practical considerations.临床试验中的响应自适应随机化:从误解到实际考量
Stat Sci. 2023 May;38(2):185-208. doi: 10.1214/22-STS865.
5
Recent innovations in adaptive trial designs: A review of design opportunities in translational research.适应性试验设计的最新创新:转化研究中的设计机会综述。
J Clin Transl Sci. 2023 Apr 28;7(1):e125. doi: 10.1017/cts.2023.537. eCollection 2023.
6
The future of psychopharmacology: a critical appraisal of ongoing phase 2/3 trials, and of some current trends aiming to de-risk trial programmes of novel agents.精神药理学的未来:对正在进行的2/3期试验以及旨在降低新型药物试验项目风险的一些当前趋势的批判性评估。
World Psychiatry. 2023 Feb;22(1):48-74. doi: 10.1002/wps.21056.
7
Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health.微随机试验:为改善公众健康开发即时自适应干预措施。
Am J Public Health. 2023 Jan;113(1):60-69. doi: 10.2105/AJPH.2022.307150. Epub 2022 Nov 22.
8
Adaptive Enrichment Designs in Clinical Trials.临床试验中的适应性富集设计
Annu Rev Stat Appl. 2021 Mar;8(1):393-411. doi: 10.1146/annurev-statistics-040720-032818.
9
The benefits of covariate adjustment for adaptive multi-arm designs.协变量调整对自适应多臂设计的好处。
Stat Methods Med Res. 2022 Nov;31(11):2104-2121. doi: 10.1177/09622802221114544. Epub 2022 Jul 25.
10
Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses.贝叶斯决策理论随机化程序的推广及延迟响应的影响。
Comput Stat Data Anal. 2022 Oct;174:107407. doi: 10.1016/j.csda.2021.107407.