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用于II期和预试验研究的精确k阶段成组序贯设计样本。

Samples of exact k-stage group sequential designs for Phase II and Pilot studies.

作者信息

Kepner James L, Chang Myron N

机构信息

Biostatistics, Department of Biostatistics, University of Florida, Gainesville, FL 32601-3330, USA.

出版信息

Control Clin Trials. 2004 Jun;25(3):326-33. doi: 10.1016/j.cct.2004.03.004.

Abstract

That the test of H(0): p=p(0) versus H(1): p>p(0) can be based on a binomially distributed random variable is widely known among users of statistical methods. What is not generally known is that under certain very general conditions, it is possible to find an exact k-stage group sequential test whose total sample size is bounded above by the sample size for the single stage binomial test. That is, it is possible to find k-stage tests for detecting H(1) for which the sum of the sample sizes at each of the stages is bounded above by the sample size for the standard binomial test. This result is somewhat remarkable since the total sample size under the group sequential test setting can be strictly less than the sample size for the uniformly most powerful (UMP) one-stage binomial test. In other words, exact group sequential tests cannot only save the average sample size but can also save the maximum sample size when they are compared to the standard binomial test. In this paper, implications of existing theory are explored and a web application written by the authors is presented. No new theory is established. Applications are described and methods are demonstrated that use the web application to rapidly create efficient designs for Phase II and Pilot studies that put a minimum number of patients at risk and that facilitate the rapid progression through a scientific research agenda. While couched here in the context of clinical trials, the results may be used in any field of inquiry where inferences are made based on the size of a binomial random variable.

摘要

统计方法的使用者普遍知晓,针对原假设(H(0): p = p(0))与备择假设(H(1): p > p(0))的检验可基于二项分布随机变量。但一般鲜为人知的是,在某些非常一般的条件下,有可能找到一种精确的(k)阶段成组序贯检验,其总样本量以单阶段二项检验的样本量为上界。也就是说,有可能找到用于检测(H(1))的(k)阶段检验,其各阶段样本量之和以标准二项检验的样本量为上界。这一结果颇为显著,因为在成组序贯检验设置下的总样本量可能严格小于一致最强大(UMP)单阶段二项检验的样本量。换句话说,与标准二项检验相比,精确的成组序贯检验不仅能节省平均样本量,还能节省最大样本量。本文探讨了现有理论的影响,并展示了作者编写的一个网络应用程序。未建立新的理论。描述了应用,并展示了使用该网络应用程序为II期和预试验快速创建高效设计的方法,这些设计使处于风险中的患者数量最少,并有助于快速推进科研议程。虽然这里是以临床试验为背景阐述的,但这些结果可用于任何基于二项随机变量大小进行推断的研究领域。

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