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Variable selection and model choice in structured survival models

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  • Benjamin Hofner
  • Torsten Hothorn
  • Thomas Kneib

Abstract

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. Copyright Springer-Verlag 2013

Suggested Citation

  • Benjamin Hofner & Torsten Hothorn & Thomas Kneib, 2013. "Variable selection and model choice in structured survival models," Computational Statistics, Springer, vol. 28(3), pages 1079-1101, June.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:3:p:1079-1101
    DOI: 10.1007/s00180-012-0337-x
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    References listed on IDEAS

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    1. Thomas Kneib & Ludwig Fahrmeir, 2007. "A Mixed Model Approach for Geoadditive Hazard Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 207-228, March.
    2. Patrick Royston & Douglas G. Altman, 1994. "Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(3), pages 429-453, September.
    3. Thomas Kneib & Torsten Hothorn & Gerhard Tutz, 2009. "Variable Selection and Model Choice in Geoadditive Regression Models," Biometrics, The International Biometric Society, vol. 65(2), pages 626-634, June.
    4. Schmid, Matthias & Hothorn, Torsten, 2008. "Boosting additive models using component-wise P-Splines," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 298-311, December.
    5. Gerhard Tutz & Harald Binder, 2006. "Generalized Additive Modeling with Implicit Variable Selection by Likelihood-Based Boosting," Biometrics, The International Biometric Society, vol. 62(4), pages 961-971, December.
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    Cited by:

    1. Holger Reulen & Thomas Kneib, 2016. "Boosting multi-state models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(2), pages 241-262, April.
    2. Kevin He & Ji Zhu & Jian Kang & Yi Li, 2022. "Stratified Cox models with time‐varying effects for national kidney transplant patients: A new blockwise steepest ascent method," Biometrics, The International Biometric Society, vol. 78(3), pages 1221-1232, September.
    3. Riccardo De Bin, 2016. "Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost," Computational Statistics, Springer, vol. 31(2), pages 513-531, June.

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