再随机测试中的重复次数。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-10-16 DOI:10.1002/pst.2438
Yilong Zhang, Yujie Zhao, Bingjun Wang, Yiwen Luo
{"title":"再随机测试中的重复次数。","authors":"Yilong Zhang, Yujie Zhao, Bingjun Wang, Yiwen Luo","doi":"10.1002/pst.2438","DOIUrl":null,"url":null,"abstract":"<p><p>In covariate-adaptive or response-adaptive randomization, the treatment assignment and outcome can be correlated. Under this situation, the re-randomization test is a straightforward and attractive method to provide valid statistical inferences. In this paper, we investigate the number of repetitions in tests. This is motivated by a group sequential design in clinical trials, where the nominal significance bound can be very small at an interim analysis. Accordingly, re-randomization tests lead to a very large number of required repetitions, which may be computationally intractable. To reduce the number of repetitions, we propose an adaptive procedure and compare it with multiple approaches under predefined criteria. Monte Carlo simulations are conducted to show the performance of different approaches in a limited sample size. We also suggest strategies to reduce total computation time and provide practical guidance in preparing, executing, and reporting before and after data are unblinded at an interim analysis, so one can complete the computation within a reasonable time frame.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Number of Repetitions in Re-Randomization Tests.\",\"authors\":\"Yilong Zhang, Yujie Zhao, Bingjun Wang, Yiwen Luo\",\"doi\":\"10.1002/pst.2438\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In covariate-adaptive or response-adaptive randomization, the treatment assignment and outcome can be correlated. Under this situation, the re-randomization test is a straightforward and attractive method to provide valid statistical inferences. In this paper, we investigate the number of repetitions in tests. This is motivated by a group sequential design in clinical trials, where the nominal significance bound can be very small at an interim analysis. Accordingly, re-randomization tests lead to a very large number of required repetitions, which may be computationally intractable. To reduce the number of repetitions, we propose an adaptive procedure and compare it with multiple approaches under predefined criteria. Monte Carlo simulations are conducted to show the performance of different approaches in a limited sample size. We also suggest strategies to reduce total computation time and provide practical guidance in preparing, executing, and reporting before and after data are unblinded at an interim analysis, so one can complete the computation within a reasonable time frame.</p>\",\"PeriodicalId\":19934,\"journal\":{\"name\":\"Pharmaceutical Statistics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pharmaceutical Statistics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/pst.2438\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pst.2438","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

摘要

在协变量适应性随机化或反应适应性随机化中,治疗分配和结果可能是相关的。在这种情况下,重新随机化检验是提供有效统计推论的一种直接而有吸引力的方法。在本文中,我们研究了测试中的重复次数。这是由临床试验中的分组顺序设计引起的,在这种情况下,中期分析的名义显著性界限可能非常小。因此,重新随机化测试会导致大量的重复测试,这在计算上可能是难以处理的。为了减少重复次数,我们提出了一种自适应程序,并在预定义标准下与多种方法进行了比较。我们进行了蒙特卡罗模拟,以显示不同方法在有限样本量下的性能。我们还提出了减少总计算时间的策略,并为中期分析中数据解盲前后的准备、执行和报告提供了实用指导,以便在合理的时间范围内完成计算。
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
Number of Repetitions in Re-Randomization Tests.

In covariate-adaptive or response-adaptive randomization, the treatment assignment and outcome can be correlated. Under this situation, the re-randomization test is a straightforward and attractive method to provide valid statistical inferences. In this paper, we investigate the number of repetitions in tests. This is motivated by a group sequential design in clinical trials, where the nominal significance bound can be very small at an interim analysis. Accordingly, re-randomization tests lead to a very large number of required repetitions, which may be computationally intractable. To reduce the number of repetitions, we propose an adaptive procedure and compare it with multiple approaches under predefined criteria. Monte Carlo simulations are conducted to show the performance of different approaches in a limited sample size. We also suggest strategies to reduce total computation time and provide practical guidance in preparing, executing, and reporting before and after data are unblinded at an interim analysis, so one can complete the computation within a reasonable time frame.

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
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
期刊最新文献
Beyond the Fragility Index. A Model-Based Trial Design With a Randomization Scheme Considering Pharmacokinetics Exposure for Dose Optimization in Oncology. Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data. Subgroup Identification Based on Quantitative Objectives. A Bayesian Dynamic Model-Based Adaptive Design for Oncology Dose Optimization in Phase I/II Clinical Trials.
×
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