当前位置: X-MOL 学术Stat. Biopharm. Res. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Note on the Promising Zone Approach in Adaptive Trial Design
Statistics in Biopharmaceutical Research ( IF 1.8 ) Pub Date : 2020-09-14 , DOI: 10.1080/19466315.2020.1811145
Jin Wang 1 , Qian Ren 1
Affiliation  

Abstract

The promising zone approach in adaptive trial design has gained popularity since its inception due to the use of the conventional test statistic at the final analysis stage rather than a weighted version that seemingly penalizes the second stage data when the adaptive trial strategy activates a sample size increase. However, this perceived advantage suffers loss of efficiency. This article is to show through mathematical derivation that the weighted test statistic by Cui, Huang, and Wang (CHW) is uniformly more powerful than the Mehta and Pocock method in the statistical promising zone. Due to relatively small chance to fall into the statistical promising zone, in practice, a wider zone defined by the minimally clinically meaningful treatment effect and the target conditional power could be used. The proposed approach is a hybrid approach by which the CHW test statistic is applied within a chosen promising zone (could be wider than the statistical promising zone), outside which the conventional test statistic is applied without a sample size increase. Simulation studies are recommended to facilitate the application of this hybrid approach.



中文翻译:

关于自适应试验设计中的有希望的区域方法的注释

摘要

由于在最终分析阶段使用了传统的检验统计量,而不是在自适应试验策略激活样本量增加时看似惩罚第二阶段数据的加权版本,自适应试验设计中的有前途的区域方法自一开始就受到欢迎. 然而,这种感知优势会降低效率。本文旨在通过数学推导表明,Cui、Huang 和 Wang (CHW) 的加权检验统计量在统计前景区域内比 Mehta 和 Pocock 方法更强大。由于落入统计有希望区域的机会相对较小,因此在实践中,可以使用由最小临床意义的治疗效果和目标条件能力定义的更宽区域。所提出的方法是一种混合方法,通过该方法,CHW 测试统计量应用于选定的有希望的区域(可能比统计的有希望的区域更宽),在该区域之外应用传统的测试统计量而不增加样本量。建议进行模拟研究以促进这种混合方法的应用。

更新日期:2020-09-14
down
wechat
bug