Cookie Settings

We use cookies to optimize our website. These include cookies that are necessary for the operation of the site, as well as those that are only used for anonymous statistic. You can decide for yourself which categories you want to allow. Further information can be found in our data privacy protection .

Essential

These cookies are necessary to run the core functionalities of this website and cannot be disabled.

Name Webedition CMS
Purpose This cookie is required by the CMS (Content Management System) Webedition for the system to function correctly. Typically, this cookie is deleted when the browser is closed.
External media

Content from external media platforms is blocked by default. If cookies from external media are accepted, access to this content no longer requires manual consent.

Name YouTube
Purpose Show YouTube content
Name Twitter
Purpose activate Twitter Feeds

Division of Biostatistics

Prof. Dr. Annette Kopp-Schneider

The Principle of Statistical Inference
© dkfz.de

The mission of the Division of Biostatistics is to support DKFZ scientists in performing and publishing excellent reproducible research. Biostatistics is an interdisciplinary science with the aim to provide efficient design of experiments and trials, and devise sound statistical analysis and interpretation of biomedical data. Adequate experimental design and analysis strategies are rarely available ‘off the shelf’ but must be developed and tailored to the specific problem in collaboration with the biomedical researcher. Therefore, the Division of Biostatistics can only provide state-of-the-art support if it actively performs methodological research and implements newly developed analysis strategies. As a consequence, it acts as a research division with a service function.

Our methodological research activities cover a wide range of biostatistical topics, often motivated and interlinked with long-standing collaborations within and outside the DKFZ, including a large number of clinical trials. The close collaboration with biomedical researchers and clinicians allows us to link statistical methodological research and clinical practice, thus contributing to the advancement of translational oncology and precision oncology. Major areas of current research interest include: design and analysis of clinical trials, both in the frequentist setting as well as in the Bayesian framework; identification of prognostic and particularly predictive factors from clinical and molecular data; optimal design and analysis for dose-response relationships, with a focus on combination of substances; measuring dependence between sets of random variables for various data types. We are keen on approaching novel methodological challenges, and indeed, in our collaborations with biomedical scientists, we address a variety of additional research topics. More detailed information about our research activities are given here.

The working group “Statistics in translational research” within the Division of Biostatistics supports clinical trial groups as biometric center and bridges research on molecular patient characteristics to new therapeutic options in oncology.

Contact

Prof. Dr. Annette Kopp-Schneider
Biostatistics (C060)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 280
69120 Heidelberg
Tel: +49 6221 42 2391

Selected Publications

  • Bloehdorn J, Krzykalla J, … Benner A* & Stilgenbauer S* Integrative prognostic models predict long-term survival after immunochemotherapy in chronic lymphocytic leukemia patients. Haematologica 107.3: 615 (2022). * equal contribution
  • Edelmann D, Richards D, Vogel D. The distance standard deviation. Ann Stat 48: 3395-3416 (2020)
  • Kopp-Schneider A, Calderazzo S, Wiesenfarth M. Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control. Biom J 62(2):361-374 (2020).
  • Maas SLN*, Stichel D*, Hielscher T* et al., Integrated molecular-morphologic meningioma classification: a multicenter retrospective analysis, retrospectively and prospectively validated. J Clin Oncol 39 (34):3839-3852 (2021). * equal contribution
to top
powered by webEdition CMS