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Tree-based modeling of time-varying coefficients in discrete time-to-event models

  • Marie-Therese PuthEmail author
  • Gerhard Tutz
  • Nils Heim
  • Eva Münster
  • Matthias Schmid
  • Moritz Berger
Article

Abstract

Hazard models are popular tools for the modeling of discrete time-to-event data. In particular two approaches for modeling time dependent effects are in common use. The more traditional one assumes a linear predictor with effects of explanatory variables being constant over time. The more flexible approach uses the class of semiparametric models that allow the effects of the explanatory variables to vary smoothly over time. The approach considered here is in between these modeling strategies. It assumes that the effects of the explanatory variables are piecewise constant. It allows, in particular, to evaluate at which time points the effect strength changes and is able to approximate quite complex variations of the change of effects in a simple way. A tree-based method is proposed for modeling the piecewise constant time-varying coefficients, which is embedded into the framework of varying-coefficient models. One important feature of the approach is that it automatically selects the relevant explanatory variables and no separate variable selection procedure is needed. The properties of the method are investigated in several simulation studies and its usefulness is demonstrated by considering two real-world applications.

Keywords

Discrete time-to-event data Time-varying coefficients Recursive partitioning Semiparametric regression Survival analysis 

Notes

Acknowledgements

This paper uses data from the German Family Panel pairfam, coordinated by Josef Brüderl, Karsten Hank, Johannes Huinink, Bernhard Nauck, Franz Neyer, and Sabine Walper. Pairfam is funded as long-term project by the German Research Foundation (DFG).

Funding

The work was supported by the German Research Foundation (DFG), Grant SCHM 2966/2-1.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Medical Biometry, Informatics and Epidemiology, Faculty of MedicineUniversity of BonnBonnGermany
  2. 2.Institute of General Practice and Family Medicine, Faculty of MedicineUniversity of BonnBonnGermany
  3. 3.Department of StatisticsLudwig-Maximilians-University MunichMunichGermany
  4. 4.Department of Oral and Cranio-Maxillo and Facial Plastic SurgeryUniversity Hospital BonnBonnGermany

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