6th CEN Conference

Session

Censored data 3

20 May 2026, 10:45

Room 13 B

Conveners

Censored data 3

• Jan Beyersmann (Ulm University)

112. Inference in pseudo-observation-based regression using (biased) covariance estimation and naive bootstrapping

Simon Mack (RWTH Aachen University)

20/05/2026, 10:45

oral presentation

The pseudo-observation regression approach provides a flexible alternative to the omnipresent proportional hazards model when modeling time-to-event outcomes. In this approach, estimands representable as expectations are fitted to regression models using covariates of interest. Exemplary estimands that fit this framework are the restricted mean time lost (in competing risks models) or the...

173. Evaluating selective‐inference methods for Lasso in survival analysis: a comparative simulation study

Lena Schemet (University of Augsburg)

20/05/2026, 11:03

oral presentation

Occam’s Razor suggests that, among several plausible explanations for a phenomenon, the simplest is preferable. Applied to regression analysis, this implies that the smallest model that fits the data is best. Therefore, in terms of analyzing high-dimensional time-to-event data, variable selection techniques are required, if we want to follow the principle of Occam's Razor. A widely used...

176. Evaluation of Joint Models and Related Approaches for Long-Term Risk Prediction from Short-Term Data

Moritz Madern (Medical University of Vienna, Center for Medical Data Science, Institute of Clinical Biometrics)

20/05/2026, 11:21

oral presentation

We consider the following prediction problem using observational data obtained from routine health-care visits. Biomarkers such as blood pressure and cholesterol are repeatedly measured over time, resulting in sparse and irregular longitudinal data for thousands of individuals. In addition, we observe corresponding survival outcomes, such as the time to cardiovascular disease or death, which...

190. Comparing variable selection in Cox and accelerated failure time models: noncollapsibility, the phantom hazard

Lorena Hafermann (Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin)

20/05/2026, 11:39

oral presentation

In descriptive studies, where the primary goal is to identify key predictors of a time-to-event outcome, and in predictive research involving numerous candidate predictors, data-driven variable selection methods are often employed to narrow down the pool of variables. This is particularly necessary when domain expertise is limited or when the practical utility of a prediction model is...