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Article

Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques

1
Chair of Spatial Data Science and Statistical Learning, Georg-August-Universität Göttingen, 37073 Göttingen, Germany
2
Department of Medical Biometrics, Informatics and Epidemiology, University Hospital Bonn, 53127 Bonn, Germany
*
Author to whom correspondence should be addressed.
Mathematics 2023, 11(2), 411; https://doi.org/10.3390/math11020411
Submission received: 28 November 2022 / Revised: 5 January 2023 / Accepted: 9 January 2023 / Published: 12 January 2023
(This article belongs to the Special Issue Recent Advances in Computational Statistics)

Abstract

Modeling longitudinal data (e.g., biomarkers) and the risk for events separately leads to a loss of information and bias, even though the underlying processes are related to each other. Hence, the popularity of joint models for longitudinal and time-to-event-data has grown rapidly in the last few decades. However, it is quite a practical challenge to specify which part of a joint model the single covariates should be assigned to as this decision usually has to be made based on background knowledge. In this work, we combined recent developments from the field of gradient boosting for distributional regression in order to construct an allocation routine allowing researchers to automatically assign covariates to the single sub-predictors of a joint model. The procedure provides several well-known advantages of model-based statistical learning tools, as well as a fast-performing allocation mechanism for joint models, which is illustrated via empirical results from a simulation study and a biomedical application.
Keywords: joint modeling; time-to-event analysis; gradient boosting; statistical learning; variable selection joint modeling; time-to-event analysis; gradient boosting; statistical learning; variable selection

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MDPI and ACS Style

Griesbach, C.; Mayr, A.; Bergherr, E. Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques. Mathematics 2023, 11, 411. https://doi.org/10.3390/math11020411

AMA Style

Griesbach C, Mayr A, Bergherr E. Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques. Mathematics. 2023; 11(2):411. https://doi.org/10.3390/math11020411

Chicago/Turabian Style

Griesbach, Colin, Andreas Mayr, and Elisabeth Bergherr. 2023. "Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques" Mathematics 11, no. 2: 411. https://doi.org/10.3390/math11020411

APA Style

Griesbach, C., Mayr, A., & Bergherr, E. (2023). Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques. Mathematics, 11(2), 411. https://doi.org/10.3390/math11020411

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