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Institute of Clinical Biometrics

Statistical Modelling for Description, Prediction and Explanation


Clinical Biometrics is the methodology of empirical medical research. Medical data can be modelled with statistical methods to reveal and concisely describe associations of characteristics of persons or their treatment with medical outcomes, to predict individual outcomes by characteristics, or to quantify causal effects of interventions on medical outcomes. Hence, statistical models contribute to the build the evidence base of medical knowledge.


By cooperating with research groups of the university’s clinics and the Center for Public Health we apply biometrical methods on medical data. We provide our expertise in single consultations, project-based collaborations and long-term partnerships in joint research projects.

Research Focus Areas

Our research focus areas are statistical model building, development and validation of prediction models, methods of causal inference from non-randomized observational studies and modelling of partially observed failure times (survival analysis). We not only follow but also contribute to the international developments in these highly evolving fields of research in biostatistics. We also provide software to the public to make our methodological developments available for routine analyses. Our Institute has high international reputation and a strong international network.

Further Information

The Institute was founded in 1991 by Prof. Michael Schemper as a section of the Department of Medical Computer Sciences within the Medical School of the University of Vienna. After the retirement of Prof. Schemper in 2015, Georg Heinze followed as the head of the Institute. The members of the Institute are highly engaged in teaching biostatistics in various programmes of the Medical University, including the undergraduate programmes of Medicine, Dentistry and Medical Informatics, and the PhD thematic programmes Medical Informatics, Biostatistics and Complex Systems, Public Health, and Epidemiology.