Speaker
Description
The servEB project (WISS 2025, federal state of Salzburg, 20102/F2300645-FPR) combines
clinical expertise, advanced statistical analyses, and AI-driven imagine classification technology to improve the assessment of trial outcomes in rare diseases, especially Epidermolysis Bullosa research as an example. When defining meaningful endpoints, multiple aspects of the disease have to be considered, including quantitative, validated outcomes (e.g., number of lesions) as well as patient-relevant outcomes (e.g., quality of life, pain, pruritus). Accordingly, the statistical analysis approach should appropriately account for the multi-faceted characteristics of those outcomes. Therefore, we address this challenge by systematically comparing a range of uni- and multivariate statistical methods with respect to both their empirical performance (i.e., type-I-error rates and power) as well as the interpretation and the properties of the respective estimands. Results indicate type-I-error control of the evaluated nonparametric approaches at the two-sided 5% level and good performance in terms of empirical power in moderate to large sample sizes. Specifically, the R package npmv yielded stable and competitive power at both very small and large sample sizes. Semiparametric MANOVA achieved the highest power but with a highly liberal type-I-error rate. First results look promising with respect to the potential of significant improvements in clinical trial design and patient care.
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