Time-To-Event Analysis
Many clinical and epidemiological studies are defined by a longitudinal design, collecting data from patients during a pre-defined time period with regular follow-up visits. In these studies, researchers are often interested in a set of target events (such as death or disease progression) that might be experienced by the study participants at some time after the beginning of the study. The analysis of such „time-to-event“ data is often challenging, as it is usually not possible to record all target events during the study period (for instance, because some participants might have left the study before having experienced any of the events). This phenomenon is called „censoring“. Our group is interested in statistical methods for the analysis of censored data, having a focus on so-called „discrete“ event times that arise from data collection at a fixed set of follow-up visits (…Buch). For example, we developed methods to model the incidence of … in the presence of one or more „competing“ target events (…). Furthermore, we are active in the analysis of performance measures for time-to-event models (like discrimination and calibration, …).