Demonstration project in the European Joint Programme on Rare Diseases (EJP RD)
This demonstration project aims to show the usability and capability of the recently developed innovative statistical methodologies for clinical trials in rare diseases. We do not re-analyze or question the original analysis of data from the randomized controlled clinical trials, but rather re-evaluate data that lacked efficiency because it was analyzed with classical statistical methodology.
In this context we investigate tuberous sclerosis complex (TSC), affecting 1 in 6.000 live births, which is characterized by the development of multisystem tumors. A previous randomized clinical trial (EPISTOP) aimed to validate the effect of preventive therapy in patients with TSC diagnosed before clinical seizures with abnormal EEG, versus late standard therapy of epilepsy, administered after the seizure onset. This preventive therapy resulted in a significant better outcome in seizures and co-morbidities. However, this trial included few patients and did not allow to fully explore the secondary endpoints. In the project our aim is to demonstrate the added value of new methodologies in TSC for an optimal use of all available data (RCT, observational and external data collected with the same protocol).
Development of an efficient method for scanning and visualizing the space of grain boundary geometry and composition: Atomistic simulations in combination with statistical methodology Modern methods for optimizing material properties
Modern methods for the optimization of material properties make increasing use of data-based research methods. The German Research Foundation (DFG) is funding joint research work in this area at the Chair of Stochastics (Statistics) of the Faculty of Mathematics and the Interdisciplinary Centre for Advanced Materials Simulation (ICAMS) with approx. 500 000 EUR for 3 years.
Grain boundaries in metallic microstructures have a decisive influence on the mechanical and functional properties of the material and can be specifically modified by segregation of foreign or alloying atoms, which leads to a change in the grain boundary energy. The scientists from the research groups of Holger Dette (Faculty of Mathematics) and Rebecca Janisch (ICAMS) are working on the mathematical modeling of these processes. The goal of the research is to develop an efficient high-throughput method for numerical simulations to determine grain boundary and segregation energies. For the development of the new algorithms, the scientists use methods from mathematical statistics, representation theory (for modeling grain boundary symmetries) and optimization.
