6th CEN Conference

Session

Streitberg- and Lienert-Awards

19 May 2026, 15:45

Room 13 A

Conveners

Streitberg- and Lienert-Awards

• Anne-Laure Boulesteix (LMU Munich)
• Jan Beyeresmann (Ulm University)

250. Optimal designs for non-linear segmented regression

Jan-Bernd Igelmann (TU Dortmund University)

19/05/2026, 15:45

oral presentation

Optimal designs maximize the experimental efficiency and precision, but are sometimes difficult to obtain, especially in cases with non-trivial underlying model functions. A possible application area providing the motivating example is toxicology. Liver carcinoma cells are modelled as a function of valproic acid (VPA) concentration using the common four-parameter log-logistic (4PLL) model. To...

253. Adaptive group sequential designs for event-driven survival trials

Alexandra Nagel (University of Ulm, METRONOMIA Clinical Research GmbH)

19/05/2026, 16:03

oral presentation

The concept of interim analysis and adaption is more and more used in clinical trials. Furthermore, one often has several endpoints such as progression-free survival (PFS) and overall survival (OS) which is discussed in the current paper of Danzer et al. (2022). There, testing hypotheses with adaptive group sequential one-sample tests for the distribution of PFS and OS is developed. The...

255. A Statistical Approach to Latent Dynamic Modeling with Differential Equations

Maren Hackenberg (Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg. Freiburg Center for Data Analysis, Modeling, and AI, University of Freiburg)

19/05/2026, 16:21

oral presentation

In clinical registries, longitudinal patient data are often sparse, noisy, and irregularly sampled, yet an important practical question is how an individual patient’s health status is likely to change from the current visit to the next.

Regression-based approaches to longitudinal data analysis typically provide a global fit over the full observed time course, but are not directly aimed at...

254. Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data

Kaya Miah (Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany)

19/05/2026, 16:39

oral presentation

In the era of precision medicine with increasing molecular information, the use of a multi-state model is required to capture the individual disease pathway along with underlying etiologies with greater precision. Especially the availability of big data with numerous covariates induces several statistical challenges for model building. For multi-state models based on high-dimensional data,...