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Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost

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  • Riccardo De Bin

    (University of Munich)

Abstract

Despite the limitations imposed by the proportional hazards assumption, the Cox model is probably the most popular statistical tool used to analyze survival data, thanks to its flexibility and ease of interpretation. For this reason, novel statistical/machine learning techniques are usually adapted to fit its requirements, including boosting. Boosting is an iterative technique originally developed in the machine learning community to handle classification problems, and later extended to the statistical field, where it is used in many situations, including regression and survival analysis. The popularity of boosting has been further driven by the availability of user-friendly software such as the R packages mboost and CoxBoost, both of which allow the implementation of boosting in conjunction with the Cox model. Despite the common underlying boosting principles, these two packages use different techniques: the former is an adaptation of model-based boosting, while the latter adapts likelihood-based boosting. Here we contrast these two boosting techniques as implemented in the R packages from an analytic point of view; we further examine solutions adopted within these packages to treat mandatory variables, i.e. variables that—for several reasons—must be included in the model. We explore the possibility of extending solutions currently only implemented in one package to the other. A simulation study and a real data example are added for illustration.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:compst:v:31:y:2016:i:2:d:10.1007_s00180-015-0642-2
    DOI: 10.1007/s00180-015-0642-2
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    References listed on IDEAS

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    1. Benjamin Hofner & Andreas Mayr & Nikolay Robinzonov & Matthias Schmid, 2014. "Model-based boosting in R: a hands-on tutorial using the R package mboost," Computational Statistics, Springer, vol. 29(1), pages 3-35, February.
    2. 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.
    3. 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.
    4. Tutz, Gerhard & Binder, Harald, 2007. "Boosting ridge regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6044-6059, August.
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    Cited by:

    1. Battauz, Michela & Vidoni, Paolo, 2022. "A likelihood-based boosting algorithm for factor analysis models with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    2. Yanis Tazi & Juan E. Arango-Ossa & Yangyu Zhou & Elsa Bernard & Ian Thomas & Amanda Gilkes & Sylvie Freeman & Yoann Pradat & Sean J. Johnson & Robert Hills & Richard Dillon & Max F. Levine & Daniel Le, 2022. "Unified classification and risk-stratification in Acute Myeloid Leukemia," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Heidi Seibold & Christoph Bernau & Anne-Laure Boulesteix & Riccardo De Bin, 2018. "On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models," Computational Statistics, Springer, vol. 33(3), pages 1195-1215, September.
    4. Riccardo De Bin & Vegard Grødem Stikbakke, 2023. "A boosting first-hitting-time model for survival analysis in high-dimensional settings," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 420-440, April.

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