Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, Berlin, Germany.
Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, Berlin, Germany.
Stat Methods Med Res. 2021 Mar;30(3):687-701. doi: 10.1177/0962280220970228. Epub 2020 Nov 24.
In many experiments and especially in translational and preclinical research, sample sizes are (very) small. In addition, data designs are often high dimensional, i.e. more dependent than independent replications of the trial are observed. The present paper discusses the applicability of -test-type statistics (multiple contrast tests) in high-dimensional designs (repeated measures or multivariate) with small sample sizes. A randomization-based approach is developed to approximate the distribution of the maximum statistic. Extensive simulation studies confirm that the new method is particularly suitable for analyzing data sets with small sample sizes. A real data set illustrates the application of the methods.
在许多实验中，尤其是在转化和临床前研究中，样本量非常小。此外，数据设计通常是高维的，即试验的重复比独立复制观察到的更多。本文讨论了在小样本量的高维设计（重复测量或多变量）中应用 t 检验型统计量（多重比较检验）的适用性。开发了一种基于随机化的方法来近似最大统计量的分布。广泛的模拟研究证实，新方法特别适用于分析小样本量数据集。一个真实数据集说明了该方法的应用。
