分组顺序多臂多阶段生存试验设计与治疗选择。

IF 1.2 4区 医学 Q4 PHARMACOLOGY & PHARMACY Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-07-16 DOI:10.1080/10543406.2023.2235409
Jianrong Wu, Yimei Li
{"title":"分组顺序多臂多阶段生存试验设计与治疗选择。","authors":"Jianrong Wu, Yimei Li","doi":"10.1080/10543406.2023.2235409","DOIUrl":null,"url":null,"abstract":"<p><p>Multi-arm trials are increasingly of interest because for many diseases; there are multiple experimental treatments available for testing efficacy. Several novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining stopping boundaries. For example, the method of Jaki and Magirr for time-to-event endpoint, implemented in R package <b>MAMS</b>, requires complicated computational efforts to obtain stopping boundaries. In this study, we develop a group sequential MAMS survival trial design based on the sequential conditional probability ratio test. The proposed method is an improvement of the Jaki and Magirr's method in the following three directions. First, the proposed method provides explicit solutions for both futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational efforts for the trial design. Second, the proposed method provides an accurate number of events for the fixed sample and group sequential designs. Third, the proposed method uses a new procedure for interim analysis which preserves the study power.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"453-468"},"PeriodicalIF":1.2000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Group sequential multi-arm multi-stage survival trial design with treatment selection.\",\"authors\":\"Jianrong Wu, Yimei Li\",\"doi\":\"10.1080/10543406.2023.2235409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Multi-arm trials are increasingly of interest because for many diseases; there are multiple experimental treatments available for testing efficacy. Several novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining stopping boundaries. For example, the method of Jaki and Magirr for time-to-event endpoint, implemented in R package <b>MAMS</b>, requires complicated computational efforts to obtain stopping boundaries. In this study, we develop a group sequential MAMS survival trial design based on the sequential conditional probability ratio test. The proposed method is an improvement of the Jaki and Magirr's method in the following three directions. First, the proposed method provides explicit solutions for both futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational efforts for the trial design. Second, the proposed method provides an accurate number of events for the fixed sample and group sequential designs. Third, the proposed method uses a new procedure for interim analysis which preserves the study power.</p>\",\"PeriodicalId\":54870,\"journal\":{\"name\":\"Journal of Biopharmaceutical Statistics\",\"volume\":\" \",\"pages\":\"453-468\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Biopharmaceutical Statistics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/10543406.2023.2235409\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/7/16 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biopharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/10543406.2023.2235409","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/7/16 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

摘要

多臂试验越来越受到关注,因为对于许多疾病来说,有多种试验性治疗方法可用于疗效测试。目前已提出了几种新颖的多臂多阶段(MAMS)临床试验设计。然而,常规采用分组顺序 MAMS 的一个主要障碍是获取停止边界的计算工作量。例如,在 R 软件包 MAMS 中实现的 Jaki 和 Magirr 用于时间到事件终点的方法需要复杂的计算工作才能获得停止边界。在本研究中,我们开发了一种基于序列条件概率比检验的分组序列 MAMS 生存试验设计。所提出的方法从以下三个方面对 Jaki 和 Magirr 的方法进行了改进。首先,建议的方法为任意数量的阶段和臂提供了明确的无效和有效边界的解决方案。因此,它避免了试验设计中复杂的计算工作。其次,建议的方法为固定样本和分组顺序设计提供了准确的事件数。第三,建议的方法使用了一种新的中期分析程序,从而保持了研究功率。
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
Group sequential multi-arm multi-stage survival trial design with treatment selection.

Multi-arm trials are increasingly of interest because for many diseases; there are multiple experimental treatments available for testing efficacy. Several novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining stopping boundaries. For example, the method of Jaki and Magirr for time-to-event endpoint, implemented in R package MAMS, requires complicated computational efforts to obtain stopping boundaries. In this study, we develop a group sequential MAMS survival trial design based on the sequential conditional probability ratio test. The proposed method is an improvement of the Jaki and Magirr's method in the following three directions. First, the proposed method provides explicit solutions for both futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational efforts for the trial design. Second, the proposed method provides an accurate number of events for the fixed sample and group sequential designs. Third, the proposed method uses a new procedure for interim analysis which preserves the study power.

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
Journal of Biopharmaceutical Statistics
Journal of Biopharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.50
自引率
18.20%
发文量
71
审稿时长
6-12 weeks
期刊介绍: The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers: Drug, device, and biological research and development; Drug screening and drug design; Assessment of pharmacological activity; Pharmaceutical formulation and scale-up; Preclinical safety assessment; Bioavailability, bioequivalence, and pharmacokinetics; Phase, I, II, and III clinical development including complex innovative designs; Premarket approval assessment of clinical safety; Postmarketing surveillance; Big data and artificial intelligence and applications.
期刊最新文献
DOD-BART: machine learning-based dose optimization design incorporating patient-level prognostic factors via Bayesian additive regression trees. Revolutionizing cardiovascular disease classification through machine learning and statistical methods. The 2009 FDA PRO guidance, Potential Type I error, Descriptive Statistics and Pragmatic estimation of the number of interviews for item elicitation. BOP2-TE: Bayesian optimal phase 2 design for jointly monitoring efficacy and toxicity with application to dose optimization. Latent class analysis of post-acute sequelae of SARS-CoV-2 infection.
×
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