Working Group Statistical Methods in Epidemiology
Welcome to the website of the working group Statistical Methods in Epidemiology in the Department of Medical Biometrics, Informatics and Epidemiology (IMBIE) at the University of Bonn.
Current Job Offer
Most Recent Publications
- Staerk C, Byrd A and Mayr A (2023): Recent methodological trends in Epidemiology: No need for data-driven variable selection? American Journal of Epidemiology. kwad193.
- Schäfer V S, Recker F, Kretschmer E, Putensen C, Ehrentraut S F, Staerk C, Fleckenstein T, Mayr A, Seibel A, Schewe JC and Petzinna S M (2023): Lung Ultrasound in Predicting Outcomes in Patients with COVID-19 Treated with Extracorporeal Membrane Oxygenation. 15(9): 1796.
- Mohsen G, Strömer A, Mayr A, Kunsorg A, Stoppe C, Wittmann M and Velten M (2023): Effects of Omega-3 Fatty Acids on Postoperative Inflammatory Response: A Systematic Review and Meta-Analysis. 15(15): 3414.
- Speller J, Staerk C, Gude F and Mayr A (2023): Robust gradient boosting for generalized additive models for location, scale and shape. Advances in Data Analysis and Classifiaction. 1-20.
- Hassanin E, Maj C, Klikhammer H, Krawitz P, May P and Bobbili D R (2023): Assessing the performance of European-derived cardiometabolic polygenic risk scores in South-Asians and their interplay with family history. BMC Medical Genomics. 16: 164.
- Balestra C, Maj C, Müller E and Mayr A (2023): Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets. PloS one. https://doi.org/10.1371/journal.pone.0282699.
- Strömer A, Klein N, Staerk C, Klinkhammer H and Mayr A (2023): Boosting Multivariate Structured Additive Distributional Regression Models. Statistics in Medicine. arXiv preprint arXiv:2207.08470.
- Klinkhammer H, Staerk C, Maj C, Krawitz P M and Mayr A (2023): A statistical boosting framework for polygenic risk scores based on large-scale genotype data. Frontiers in Genetics. 13: 1076440.
- Maj C, Staerk C, Borisov O, Klinkhammer H, Yeung M W, Krawitz P, Mayr A (2022): Statistical learning for sparser fine-mapped polygenic models: the prediction of LDL-cholesterol. Genetic Epidemiology. 46(8): 589-603.
We are interested in the development of new methodology in the field of computational statistics and in the applied analysis of biomedical and epidemiological data.
- Methodological research
- Statistical boosting
- GAMLSS (generalized additive models for location, scale and shape)
- Quantile regression
- Prediction inference and prediction intervals
- Variable selection for high-dimensional data
- Applications
- Paediatric and Perinatal Epidemiology
- Eating disorders
- Schizophrenia and depression
- Medical Internet research
- Genomics
For statistical consulting and new projects, please do not hesitate to contact us if you are interested in a cooperation. We are always open to new areas of application.
We also offer various topics for Bachelor or Master theses with a focus on statistical methodology. Please get in touch with us if you are interested.
