Permutation Tests in Statistics and Clinical Trials Development: Principles, Applications, and Regulatory Considerations
Executive Summary Permutation tests represent a powerful class of non-parametric statistical methods that are increasingly vital in modern data analysis, particularly within clinical trial development. These tests derive their statistical distributions directly from the observed data through resampling, rather than relying on theoretical assumptions about data distribution. This inherent data-driven approach confers significant advantages, including robustness to outliers and non-normality, flexibility in the choice of test statistics, and the ability to yield exact p-values, especially for smaller sample sizes. Their unique alignment with the randomization process in clinical trials ensures strong internal validity, which is paramount for robust scientific conclusions. The utility of permutation tests extends across various complex clinical trial designs, including cluster randomized trials, multi-arm studies, and adaptive designs, where traditional parametric methods may falter...