Computational Statistics

, Volume 28, Issue 3, pp 1079–1101

Variable selection and model choice in structured survival models

Original Paper

DOI: 10.1007/s00180-012-0337-x

Cite this article as:
Hofner, B., Hothorn, T. & Kneib, T. Comput Stat (2013) 28: 1079. doi:10.1007/s00180-012-0337-x


We aim at modeling the survival time of intensive care patients suffering from severe sepsis. The nature of the problem requires a flexible model that allows to extend the classical Cox-model via the inclusion of time-varying and nonparametric effects. These structured survival models are very flexible but additional difficulties arise when model choice and variable selection are desired. In particular, it has to be decided which covariates should be assigned time-varying effects or whether linear modeling is sufficient for a given covariate. Component-wise boosting provides a means of likelihood-based model fitting that enables simultaneous variable selection and model choice. We introduce a component-wise, likelihood-based boosting algorithm for survival data that permits the inclusion of both parametric and nonparametric time-varying effects as well as nonparametric effects of continuous covariates utilizing penalized splines as the main modeling technique. An empirical evaluation of the methodology precedes the model building for the severe sepsis data. A software implementation is available to the interested reader.


Hazard regression Likelihood-based boosting Model choice P-splines Smooth effects Time-varying effects 

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Benjamin Hofner
    • 1
  • Torsten Hothorn
    • 2
  • Thomas Kneib
    • 3
  1. 1.Institut für Medizininformatik, Biometrie und EpidemiologieFriedrich-Alexander-Universität Erlangen-Nürnberg ErlangenGermany
  2. 2.Institut für StatistikLudwig-Maximilians-UniversitätMünchenGermany
  3. 3.Institut für Statistik und ÖkonometrieGeorg-August-UniversitätGöttingenGermany

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