Permutation tests for ASCA in multivariate longitudinal intervention studies

Permutation tests are the standard technique for significance testing in Analysis of Variance Simultaneous Component Analysis. However, there is a vast number alternative approaches permutation testing, and choices grows relation to complexity study design. In this paper, we focus on longitudinal intervention studies with multivariate outcomes, relevant experimental design clinical where outcome an omics profile (such as genomics, metabolomics, like). We propose new derive power curves tailored size (un)balanced nature data set study. This useful identify misleading tests, lack or overly optimistic outcomes. found that choosing best approach far from intuitive significant risk deriving incorrect conclusions real-life analyses. Our avoids can be extended other complex designs interest. The code available free use.