第二阶段和试点研究的精确k阶段组顺序设计样本

James L Kepner , Myron N Chang
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引用次数: 16

摘要

H0: p=p0与H1: p>p0的检验可以基于二项分布的随机变量,这在统计方法的使用者中是众所周知的。一般不知道的是,在某些非常一般的条件下,有可能找到一个精确的k阶段组序列检验,其总样本量在单阶段二项检验的样本量的上界。也就是说,有可能找到用于检测H1的k阶段检验,其中每个阶段的样本量之和的上限为标准二项检验的样本量。这一结果在某种程度上是显著的,因为在组顺序试验设置下的总样本量可以严格小于均匀最强大(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,精确组序检验不仅可以节省平均样本量,还可以节省最大样本量。本文探讨了现有理论的影响,并提出了作者编写的一个web应用程序。没有建立新的理论。应用程序描述和方法演示,使用web应用程序快速创建II期和试点研究的有效设计,使患者的最低数量的风险,并促进通过科学研究议程的快速进展。虽然这里是在临床试验的背景下提出的,但结果可以用于任何基于二项随机变量大小进行推断的调查领域。
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Samples of exact k-stage group sequential designs for Phase II and Pilot studies

That the test of H0: p=p0 versus H1: p>p0 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 H1 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.

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