Institut
Arbeitsgruppen

AG Computationale Biostatistische Modellierung

Institut für Medizininformatik, Biometrie und Epidemiologie


Team

 

Dr. sc. nat. Sebastian Meyer (Gruppenleitung)

 

Dr. rer. biol. hum. Colin Griesbach

Dr. rer. biol. hum. Tobias Hepp

Junyi Lu, M.Sc.

Anja Rappl, M.Sc.


Forschungsprofil

  • Joint Modelling longitudinaler Endpunkte und Überlebenszeiten
  • Regressionsmodelle für Verteilungen und Quantile
  • Statistische Lernverfahren, Boosting
  • Statistische Methoden in der Infektionsepidemiologie
  • Statistische Software
  • Variablenselektion in hochdimensionalen Daten


Projekte

  • DFG: Advanced statistical inference in joint models for longitudinal time-to-event data
  • Freigeist: Bayesian Boosting - A new approach to data science, unifying two statistical philosophies
  • IZKF J75: Statistical Analysis of Infectious Disease Spread
  • TIGER: Trans-sectoral intervention programme to improve geriatric care in Regensburg
  • SCOPE: Screening for chronic kidney disease among older people across Europe


Abgeschlossene Projekte

  • DFG: GAMLSS for biostatistical regression modeling - refinements and further developments
  • IZKF J61: Extending joint models in biomedical outcomes
  • IZKF J49: Extending statistical boosting algorithms for biomedical research
  • INTERBED: Internet-based guided self-help for overweight and obese patients with binge eating disorder


Ausgewählte Veröffentlichungen

  1. Griesbach, C., Groll, A. and Bergherr, E. (2021): Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques. PLoS ONE 16(7).
  2. Lu, J. and Meyer, S. (2021): An endemic-epidemic beta model for time series of infectious disease proportions. Journal of Applied Statistics.
  3. Griesbach, C., Säfken, B. and Waldmann, E. (2021): Gradient boosting for linear mixed models. International Journal of Biostatistics.
  4. Hepp, T., Zierk, J., Rauh, M., Metzler, M. and Mayr, A. (2020): Latent class distributional regression for the estimation of non-linear reference limits from contaminated data sources. BMC Bioinformatics 21:524.
  5. Lu, J. and Meyer, S. (2020): Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model. International Journal of Environmental Research and Public Health 17(4).
  6. Meyer, S. (2019): polyCub: An R package for Integration over Polygons. Journal of Open Source Software 4(34).
  7. Hepp, T., Schmid, M. and Mayr, A. (2019): Significance tests for boosted location and scale models with linear base-learners. International Journal of Biostatistics 15(1).
  8. Mayr, A., Hofner, B., Waldmann, E., Hepp, T., Meyer, S., Gefeller, O. (2017): An update on statistical boosting in biomedicine. Computational and Mathematical Methods in Medicine.
  9. Waldmann, E., Taylor-Robinson D., Klein, N., Kneib, T., Pressler, T., Schmid, M. and Mayr, A. (2017): Boosting Joint Models for Longitudinal and Time-to-Event Data. Biometrical Journal 59(6):1104-1121.
  10. Hofner, B., Mayr, A., and Schmid, M. (2016): gamboostLSS: An R package for model building and variable selection in the GAMLSS framework. Journal of Statistical Software 74(1):1-31.
  11. Mayr, A., Hofner, B. and Schmid, M. (2016): Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection. BMC Bioinformatics. 17:288.
  12. Hepp, T., Schmid, M., Gefeller, O., Waldmann, E. and Mayr, A. (2016): Approaches to regularized regression - A comparison between gradient boosting and the lasso. Methods of Information in Medicine 455(5):422-430.