A Stepwise Resampling Method of Multiple Hypothesis Testing
本文提出一种结合序贯多重检验与重抽样结构的多重假设检验方法，无需分布假设即可利用数据协方差结构，适用于两组多结局比较的离散数据，能渐近控制实验整体第一类错误概率。
Abstract This article introduces a method of multiple hypothesis testing that combines the idea of sequential multiple testing procedures with the structure of resampling methods. The method can be seen as an alternative to the analytic method of Dunnett and Tamhane, which requires a specific distributional form. Resampling incorporates the covariance structure of the data without the need for distributional assumptions. Recent work by Westfall and Young has shown that a step-down resampling method is asymptotically consistent when adjusted p values can be obtained exactly for continuous data. This article shows that in the case of a comparison of two groups on multiple outcomes, those results are generalizable to discrete data where exact adjusted p values are not available. It is shown that the method asymptotically attains the desired level for controlling the experimentwise probability of a type I error.