Department of Economics, Ca' Foscari University of Venice, Venice, Italy.
Biometris, Wageningen University and Research, Wageningen, The Netherlands.
Stat Med. 2023 Jun 30;42(14):2311-2340. doi: 10.1002/sim.9725. Epub 2023 Apr 22.
We propose a permutation-based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any selected subset of hypotheses. These bounds are simultaneously valid with high confidence. The methodology is particularly useful in functional Magnetic Resonance Imaging cluster analysis, where it provides a confidence statement on the percentage of truly activated voxels within clusters of voxels, avoiding the well-known spatial specificity paradox. We offer a user-friendly tool to estimate the percentage of true discoveries for each cluster while controlling the family-wise error rate for multiple testing and taking into account that the cluster was chosen in a data-driven way. The method adapts to the spatial correlation structure that characterizes functional Magnetic Resonance Imaging data, gaining power over parametric approaches.
我们提出了一种基于排列的方法来同时测试大量假设。我们的方法为任何选定的假设子集提供了真实发现数量的下限。这些界限具有高度置信度的同时有效。该方法在功能磁共振成像聚类分析中特别有用，它为聚类中的真实激活体素的百分比提供了置信声明，避免了众所周知的空间特异性悖论。我们提供了一个用户友好的工具，可以在控制多重测试的家族错误率的同时，估计每个聚类的真实发现百分比，并考虑到该聚类是通过数据驱动的方式选择的。该方法适应了功能磁共振成像数据的空间相关结构，相对于参数方法具有更强的功效。
