Functional Data Analysis (FDA), focusing on data composed of functions or curves, has become increasingly popular. We study reliable methods for comparing multiple groups of functional data, especially in studies involving several factors or complex designs. We introduce a new statistical approach designed for multivariate functional data. Our methods are reliable because they allow us to...
Adaptive and, in particular, group-sequential designs are well-established in clinical trials. Time-to-event endpoints pose particular challenges because individual participants can contribute data to multiple stages of the trial. Nevertheless, the log-rank test - the standard analysis method for time-to-event data - can be embedded in flexible adaptive designs (e.g. with sample-size...
Quadratic forms, such as the rank-based Wald-type statistic or the rank-based ANOVA-type statistic, are widely used to compare multivariate distributions without the necessity of parametric assumptions (like multivariate normality). These tests have two major limitations, however:
i) They are, by construction, omnibus tests and thus not able to locate which specific dimensions (variables) are...
In many trials and experiments, subjects are not only observed once, but multiple times, resulting in a cluster of possibly correlated observations. For example, mice sharing the same cage or students of the same class are typical examples of clustered data. Typically, under the assumption of normally distributed data, mixed models are used for analysis.
However, this model assumption is...
