Division of Biostatistics

The main tasks of the Biostatistics Division are service and research in the field of biostatistical methods and their application in cancer research. We provide statistical support for all scientific activities of the DKFZ from in vitro and animal studies to human subject research including clinical trials, linking methodical research and biomedical disciplines. Our support covers experimental design, sample size estimation, data analysis and preparation of results for publication. It ranges from brief statistical consultations to long-term collaborations and covers standard statistical analysis approaches as well as the development of complex statistical methods tailored to specific questions. The ongoing evolution of novel measurement techniques and platforms and the development of new research questions make it necessary to continuously refine the biostatistical and biomathematical methodology and to develop and implement new methods for analysis. Our current research areas reflect the requests we are confronted with from collaborators. We develop and assess efficient and valid methods for visualizing, integrating and analyzing data, in particular high-dimensional molecular data. We develop optimized biometrical designs in experimental cancer research and in clinical studies. We devise methods of quantitative risk assessment.

One focus of interest of our research is the evaluation of molecular data in biomarker studies. We will extend our research to find associations with clinico-pathological factors, prognosis or response to therapy with the aim to identify diagnostic, prognostic or predictive factors. We will develop and validate statistical methods for classification and prediction using low- and high-dimensional data. The development of methods for data integration is becoming an important aspect of our work, e.g. the development of Bayesian hierarchical models for classification and prediction, which can make use of multiple data sources, while properly accounting for biological inter-relations between these data. Another area of research is the development and application of statistical and stochastic models for dose-response relationships. In this context, we investigate non-linear regression models. A special focus lies on the description of cellular processes using stochastic models to understand the process of carcinogenesis or the effect of toxic compounds on cell systems.