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Conference Program


The program is still preliminary and might be subject to further adaptions.

Keynote speakers

Michael Love (University of North Carolina-Chapel Hill, USA)
Michael Love's research focuses on statistical and computational methods for the analysis of high-dimensional genetic data with the goal of facilitating biomedical and biological research. He has developed several widely used open source software packages for the analysis of RNA sequencing data, which also feature highly detailed step-by-step instructions.

Ina Rondak (European Medicines Agency, The Netherlands)
Ina Rondak is a statistician for the European Medicines Agency at the Methodology Division of the Data Analytics and Methods Task Force. Her focus is on providing scientific support for the development and evaluation of medical devices, especially using complex and innovative study designs and statistical methods.

Maarten van Smeden (University Medical Center Utrecht, The Netherlands)
Maarten van Smeden focuses on the development, validation and implementation of predictive models. Through collaborations, he has contributed to the implementation of complex methodology in various disciplines and has been involved in the development and validation of a variety of diagnostic and prognostic prediction models.



AG Meetings

Mittwoch 12:00 - 13:00 Uhr:

  • AG Statistische Methoden in der Epidemiologie
  • AG Statistische Methoden in der Medizin
  • AG Ethik und Verantwortung
  • AG Bayes-Methodik
  • AG Statistik Stochastischer Prozesse
  • AG Non-Clinical Statistics
  • AG Pharmazeutische Forschung
  • AG DAGStat

Donnerstag 12:00 - 12:40 Uhr:

  • AG Öffentlichkeitsarbeit

Donnerstag 12:40 - 13:20 Uhr:

  • AG Leitersitzung

Special sessions

Celebration of the 70th Biometric Colloquium

IBS-DR Award ceremony (Gustav-Adolf-Lienert-Preis, Bernd-Streiberg-Preis)

Statistics in Practice

Young Statisticians Session & Panel discussion

Public lecture "Sich selbst mit dem Smartphone behandeln: Wie gut funktioniert das?"

Statistics in Practice

Diagnostic accuracy studies: basic and advanced statistical methods

Antonia Zapf (UKE Hamburg)
Alexander Fierenz (UKE Hamburg)

Denise Köster (UKE Hamburg)
Philipp Weber (UKE Hamburg)

Maria Stark (
Staburo GmbH)

Invited Sessions




Four tutorials will be offered in the morning on Wednesday, February 28.

1. Multiple endpoints and prioritized outcomes- Nonparametric analysis methods using generalized pairwise comparisons

Lecturers: Edgar Brunner (University Medical Center Göttingen) and Werner Brannath (University of Bremen)

Learning objectives:
- The role of multiple endpoints in clinical trials and medical research
- Understanding Generalized Pairwise Comparisons (GPC) and related effect measures
- Relation of GPC to the Mann-Whitney test and other non-parametric effects

Target group:
 - Experienced and early career biostatisticians, clinical trial experts

 - Some knowledge in clinical trials and non-parametric statistics

- Hybrid

2. Introduction to Causal Inference and Target Trial Emulation
Lecturers: Vanessa Didelez and Malte Braitmaier (both BIPS, Bremen)

Learning objectives:
- Be able to recognise avoidable sources of bias in naive studies using observational data
- Become aware of principles techniques to address the above issues
- Acquire a basic understanding of TTE that will facilitate studying the more advanced literature

Target group:
- PhD students, Post-docs (and more seniors) in statistics and related fields who want to learn about the causal analysis of observational (real-world) data, or even about addressing intercurrent events in RCTs

- A willingness to actively participate in discussions

- Hybrid

3. An introduction to estimands and estimand-aligned estimation

Lecturers: Tobias Mütze (Novartis, Basel) and Tim Friede (University Medical Center Göttingen)

Learning objectives:
- Understand the basic concepts related to the ICH E9 (R1) addendum including estimands and their attributes as well as intercurrent events and their handling strategies
- Identify an appropriate primary analysis method that targets the estimand of interest, fully alignes with the ICH E9 addendum
- Understand how to change assumptions made for the primary analysis in sensitivity analyses
- Implement appropritate analyses and sensitivity analyses

Target group:
- Statisticians, in particular those who have limited experience with the estimand framework

- R version 4.1 or higher
- R packages: tidyverse, rbmi

- Presence only

4. Advanced data visualization  in R: (Re)producing professional plots with ggplot2 and the tidyverse

Lecturer: Paul Schmidt (BioMath GmbH, Hamburg)

Learning objectives:
- Deep dive into ggplot2: Understand the intricacies of ggplot2 for top-tier data visualization
- Data manipulation with tidyverse: Utilize packages like dplyr, tidyr, and forcats to shape data optimally for creating graphs
- Aestetic fine-tuning: Master the skills of axis formatting, theme detailing, and color selection
- Reproducing Exercise: Recreate published plots to understand the application of ggplot2 techniques in real-world scenarios
- Exporting excellence: Learn best practices for exporting plots in publication-ready formats

Target group:
- Anyone wanting to produce or present high-quality plots. Conveying complex data insights through refinde visualizations is a relevant part of statistical analysis

- Basic understanding of R programming is required
- Basci knowledge of ggplot2 and the tidyverse is beneficial, but not required
- R-packages: tidyverse, cowplot, ggrepel, ggtext, viridis

- Hybrid

Topics for contributed sessions

Design and analysis of clinical trials for drugs and medical devices

Preclinical drug development, safety and toxicology

Diagnostic and biomarker studies

Precision medicine

Missing data


Multiple testing

Statistics in epidemiology

Real world data and evidence beyond RCTs

Estimands and causal inference

Nonparametric methods

Regression and prognostic modelling

Time-to-event analysis

High-dimensional molecular data and genetic epidemiology

Machine learning/AI for medicine and biology

Computational biostatistics, software engineering

Agricultural, biological and environmental statistics

Teaching statistics, statistical literacy

Data sharing, reproducibility, open replicable science

Free topics