A Modification of the 50 %-Conditional Power Approach for Increasing the Sample Size Based on an Interim Estimate of Treatment Difference

K. Uemura, Y. Matsuyama, Y. Ohashi
{"title":"A Modification of the 50 %-Conditional Power Approach for Increasing the Sample Size Based on an Interim Estimate of Treatment Difference","authors":"K. Uemura, Y. Matsuyama, Y. Ohashi","doi":"10.5691/JJB.29.19","DOIUrl":null,"url":null,"abstract":"Recently, flexible approaches with updating of sample size during the course of clinical trials have been proposed; the weighted Z-statistic approach and the 50 %-conditional power approach. In this paper, we propose a modification of the 50 %-conditional power approach, which increases the sample size only when the conditional power based on the unblinded interim results is greater than 50 %. Our method can control the type I error rate due to the restriction on the minimum required sample size ratio under the decision of increasing sample size. Simulation studies showed that the proposed method increased power about 10 % compared with the fixed sample size design and attained higher power than the original 50 %-conditional power approach. Compared with the weighted Z-statistic approach, the proposed method had several promising operating characteristics; a substantial gain in conditional power given the decision of sample size adjustment, a low probability of reaching the maximum sample size, a substantial decrease in the conditional type II error rate given the maximum sample size, and a conservative property of not increasing sample size erroneously under no treatment effect.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Japanese journal of biometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5691/JJB.29.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Recently, flexible approaches with updating of sample size during the course of clinical trials have been proposed; the weighted Z-statistic approach and the 50 %-conditional power approach. In this paper, we propose a modification of the 50 %-conditional power approach, which increases the sample size only when the conditional power based on the unblinded interim results is greater than 50 %. Our method can control the type I error rate due to the restriction on the minimum required sample size ratio under the decision of increasing sample size. Simulation studies showed that the proposed method increased power about 10 % compared with the fixed sample size design and attained higher power than the original 50 %-conditional power approach. Compared with the weighted Z-statistic approach, the proposed method had several promising operating characteristics; a substantial gain in conditional power given the decision of sample size adjustment, a low probability of reaching the maximum sample size, a substantial decrease in the conditional type II error rate given the maximum sample size, and a conservative property of not increasing sample size erroneously under no treatment effect.
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
基于处理差异中期估计的增加样本量的50%条件功率法的修正
近年来,随着临床试验过程中样本量的更新,提出了灵活的方法;加权z统计方法和50%条件功率方法。在本文中,我们提出了一种对50% -条件功率方法的改进,只有当基于非盲期中结果的条件功率大于50%时,才会增加样本量。在增加样本量的决定下,由于对最小样本量比的限制,我们的方法可以控制第一类错误率。仿真研究表明,该方法比固定样本量设计提高了约10%的功率,并且比原来的50%条件功率方法获得了更高的功率。与加权z统计量方法相比,该方法具有较好的工作特性;在样本量调整的决策下,条件功率大幅增加,达到最大样本量的概率较低,在样本量最大的情况下,条件II型错误率大幅降低,在没有处理效果的情况下不错误增加样本量的保守性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Meta-Analysis for Time-to-event Outcome Based on Restored Individual Participant Data and Summary Statistics Theoretical Examination and Simulation Study on Analyses for Progression Free Survival as Interval-censored Data がん臨床試験と競合リスク・マルチステートモデル Robust and Interpretable Hazard-based Summary Measures of the Magnitude of the Treatment Effect and Their Inference Procedures Bayesian Ridge Estimators Based on Copula-based Joint Prior Distributions: Cox Regression Model
×
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