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Flexible Modelling of Time-Varying Exposures in Event History Analysis

MCML Authors

Andreas Bender

Dr.

→ Group Bernd Bischl
Statistical Learning and Data Science

Abstract

We present a flexible modelling approach to analyse time-varying exposures and recurrent events in team sports injuries. The approach is based on the piece-wise exponential additive mixed model where the effects of past exposures (i.e. high-intensity training loads) may accumulate over time and present complex forms of association. In order to identify a relevant time window at which past exposures have an impact on the current risk, we propose a penalty approach. We conduct a simulation study to evaluate the performance of the proposed model, under different true weight functions and different levels of heterogeneity between recurrent events. Finally, we illustrate the approach with a case study application involving an elite male football team participating in the Spanish LaLiga competition. The cohort includes time-loss injuries and external training load variables tracked by Global Positioning System devices, during the seasons 2017–2018 and 2018–2019.

inproceedings

DAGStat 2025

7th Joint Statistical Meeting of the Deutsche Arbeitsgemeinschaft Statistik. Berlin, Germany, Mar 24-28, 2025. Poster presentation. Full paper available.