A Flexible Adaptive Lasso Cox Frailty Model Based on the Full Likelihood
Abstract: In this work, a method to regularize Cox frailty models is proposed that accommodates time‐varying covariates and time‐varying coefficients and is based on the full likelihood instead of the partial likelihood. A particular advantage of this framework is that the baseline hazard can be explicitly modeled in a smooth, semiparametric way, for example, via P‐splines. Regularization for variable selection is performed via a lasso penalty and via group lasso for categorical variables while a second penalty regularizes wiggliness of smooth estimates of time‐varying coefficients and the baseline hazard. Additionally, adaptive weights are included to stabilize the estimation. The method is implemented in the R function coxlasso, which is now integrated into the package PenCoxFrail, and will be compared to other packages for regularized Cox regression.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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A Flexible Adaptive Lasso Cox Frailty Model Based on the Full Likelihood ; volume:66 ; number:7 ; year:2024 ; extent:12
Biometrical journal ; 66, Heft 7 (2024) (gesamt 12)
- Urheber
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Hohberg, Maike
Groll, Andreas
- DOI
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10.1002/bimj.202300020
- URN
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urn:nbn:de:101:1-2410081432580.048363090373
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:33 MESZ
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Beteiligte
- Hohberg, Maike
- Groll, Andreas
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