Annette Kopp-Schneider

Prof. Dr. Annette Kopp-Schneider is Head of Department at the German Cancer Research Center (DKFZ) and affiliated with the Faculty of Medicine at the University of Heidelberg. She holds a professorial rank and leads research in medical biometry with a focus on clinical trial methodology in precision oncology.

• Diploma in Mathematics, RWTH Aachen, 1984
• Dr. rer. nat. in Mathematics, RWTH Aachen, 1987
• Habilitation in Medical Biometry, University of Heidelberg, 1997

Her research interests include clinical trials for precision oncology, dose-response modeling, and biologically based mechanistic modeling of cell systems. She develops advanced statistical methods for trial design, particularly in pediatric oncology and settings with small sample sizes. Her work emphasizes rigorous control of type I error when borrowing historical data and optimal experimental designs for drug combinations.

The recent publications reflect a strong trend in methodological biostatistics, particularly in Bayesian and frequentist approaches to clinical trial design, external data incorporation, and transparent evaluation in biomedical image analysis. Her contributions span oncology, toxicology, and translational medicine, with a consistent focus on statistical rigor and reproducibility.

She has contributed to major initiatives such as the Pediatric Precision Oncology INFORM Registry, advancing personalized treatment for children with cancer. Her methodological work supports fair comparisons in hybrid and single-arm trials and improves the reliability of challenge-based research in medical imaging.

Prof. Kopp-Schneider is actively engaged in grant-funded research and mentoring within her department. She leads a team focused on biostatistical innovation in clinical research, particularly in oncology settings where data are sparse and traditional designs face limitations.

She is involved in collaborative teams working on biomedical image analysis challenges and pediatric cancer registries. Her lab contributes to open science through toolkits for challenge analysis and transparent reporting guidelines like BIAS (Transparent Reporting of Biomedical Image Analysis Challenges).