The division of Biostatistics currently focuses on six research topics:

Predictive modeling
One focus in oncology lies in the prediction of clinical endpoints from a set of candidate clinical and biological covariates. We extend standard prediction methods to include high-dimensional covariate data, and we investigate their prediction performance. Another important field of interest is competing risks and multi-state modeling.

Clinical Statistics

This research area deals with innovative methods for clinical trial designs and evaluation strategies for clinical data. Among other topics, we focus on biomarker-driven trials, statistics for personalized medicine, flexible trial designs and analyses of retrospective data collections.

Exploring Characteristics of Omics Data
Our main goal is to investigate the defining features for different types of omics data and develop appropriate statistical methods for analysis.

Bayesian modeling
Bayesian hierarchical models are used for data with complex distributions that can be explained by a multi-level (“hierarchical”) structure. This includes high-dimensional genomic data where the implementation of efficient algorithms for model inference is of importance.

Nonlinear models for mechanistic investigation and effect prediction

This research area deals with the development of mathematical models describing the processes taking place in cell systems, both naturally and in response to external interventions.


The graphical display of data is a key component of data quality control and result communication. We develop visualization techniques in different application areas.

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