ABSTRACT

We introduce an improved Bonferroni method for testing two primary endpoints in clinical trial settings using a new data-adaptive critical value that explicitly incorporates the sample correlation coefficient. Our methodology is developed for the usual Student’s t-test statistics for testing the means under normal distributional setting with unknown population correlation and variances. Specifically, we construct a confidence interval for the unknown population correlation and show that the estimated type-1 error rate of the Bonferroni method with the population correlation being estimated by its lower confidence limit can be bounded from above less conservatively than using the traditional Bonferroni upper bound. We also compare the new procedure with other procedures commonly used for the multiple testing problem addressed in this paper.

摘要

我们引入了一种改进的 Bonferroni 方法，用于使用明确包含样本相关系数的新数据自适应临界值测试临床试验设置中的两个主要终点。我们的方法是为通常的学生 t 检验统计量开发的，用于在具有未知总体相关性和方差的正态分布设置下测试均值。具体来说，我们为未知的总体相关性构建了一个置信区间，并表明与使用传统的 Bonferroni 上限相比，Bonferroni 方法的估计类型 1 错误率及其由其置信下限估计的总体相关性可以较不保守地从上方界定。边界。我们还将新程序与本文中解决的多重测试问题常用的其他程序进行了比较。