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About us

General Topics of Interest

The discipline of biostatistics is a subfield of general health sciences. It encompasses the development and application of statistical methods to a wide range of topics in biology. Safeguarding public health, researching on human health and diseases, as well as assessing health consequences, biostatistical modeling is important for life science, medical research, healthcare, patients, and the general population. By now, the academic subject around biostatistics has emancipated itself from the field of statistics.

As indicated, biostatistics or biometrics deals with a variety of issues. Biostatistics are initially to be understood as statistical theory and methods for describing, analyzing, and interpreting biological data. More precisely, biostatisticians deal with a wide range of tasks with applications in all life sciences, especially in medicine, bioinformatics, clinical statistics, toxicology, and epidemiology. Thus, it plays an important role in health promotion: For example, clinical studies and their evaluation are usually a prerequisite for the approval of prescription drugs. Further, in the context of epidemiology, various factors and influences on disease and health in individuals and populations are examined. For this purpose, statistical models are developed to explain and predict various phenomena, such as the occurrence of certain diseases, disease-related events or courses.

Biostatistics at the LMU

Currently, biostatisticians are found at various university locations in Germany, for instance at the LMU. The Department for Statistics integrates the research discipline as independent working group. The working group carries out multiple projects and research works in cooperation with external research institutions, clinics, doctors and medical professionals and other scientists. Therefore, the biostatistics team mainly deals with the following areas:

  • Development of statistical methods for meta-analyses
  • Modeling of disease dynamics
  • Development and application of new approaches for survival time analysis