Regularized parametric survival modeling to improve risk prediction models
Abstract: We propose to combine the benefits of flexible parametric survival modeling and regularization to improve risk prediction modeling in the context of time‐to‐event data. Thereto, we introduce ridge, lasso, elastic net, and group lasso penalties for both log hazard and log cumulative hazard models. The log (cumulative) hazard in these models is represented by a flexible function of time that may depend on the covariates (i.e., covariate effects may be time‐varying). We show that the optimization problem for the proposed models can be formulated as a convex optimization problem and provide a user‐friendly R implementation for model fitting and penalty parameter selection based on cross‐validation. Simulation study results show the advantage of regularization in terms of increased out‐of‐sample prediction accuracy and improved calibration and discrimination of predicted survival probabilities, especially when sample size was relatively small with respect to model complexity. An applied example illustrates the proposed methods. In summary, our work provides both a foundation for and an easily accessible implementation of regularized parametric survival modeling and suggests that it improves out‐of‐sample prediction performance.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Regularized parametric survival modeling to improve risk prediction models ; day:29 ; month:09 ; year:2023 ; extent:16
Biometrical journal ; (29.09.2023) (gesamt 16)
- Creator
-
Hoogland, J.
Debray, T. P. A.
Crowther, M. J.
Riley, Richard D.
IntHout, J.
Reitsma, J. B.
Zwinderman, Aeilko H.
- DOI
-
10.1002/bimj.202200319
- URN
-
urn:nbn:de:101:1-2023093015071374413704
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:50 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Hoogland, J.
- Debray, T. P. A.
- Crowther, M. J.
- Riley, Richard D.
- IntHout, J.
- Reitsma, J. B.
- Zwinderman, Aeilko H.
Other Objects (12)
Flexible, non-parametric modeling using regularized neural networks
Bayesian parametric models for survival prediction in medical applications
Regularized regressions for parametric models based on separated representations
Generalized kernel regularized least squares estimator with parametric error covariance
A DNA methylation signature to improve survival prediction of gastric cancer
Iassopack: Model Selection and Prediction with Regularized Regression in Stata
Spring-assisted posterior vault expansion: a parametric study to improve the intracranial volume increase prediction
Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization
Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression
A novel DNA methylation signature to improve survival prediction of progression-free survival for testicular germ cell tumors
Data-driven prediction of tool wear using Bayesian-regularized artificial neural networks
Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation
Flexible, non-parametric modeling using regularized neural networks
Bayesian parametric models for survival prediction in medical applications
Regularized regressions for parametric models based on separated representations
Generalized kernel regularized least squares estimator with parametric error covariance
A DNA methylation signature to improve survival prediction of gastric cancer
Iassopack: Model Selection and Prediction with Regularized Regression in Stata
Spring-assisted posterior vault expansion: a parametric study to improve the intracranial volume increase prediction
Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization
Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression
A novel DNA methylation signature to improve survival prediction of progression-free survival for testicular germ cell tumors
Data-driven prediction of tool wear using Bayesian-regularized artificial neural networks
Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation
Flexible, non-parametric modeling using regularized neural networks
Bayesian parametric models for survival prediction in medical applications
Regularized regressions for parametric models based on separated representations
Generalized kernel regularized least squares estimator with parametric error covariance
A DNA methylation signature to improve survival prediction of gastric cancer
Iassopack: Model Selection and Prediction with Regularized Regression in Stata
Spring-assisted posterior vault expansion: a parametric study to improve the intracranial volume increase prediction
Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization
Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression
A novel DNA methylation signature to improve survival prediction of progression-free survival for testicular germ cell tumors
Data-driven prediction of tool wear using Bayesian-regularized artificial neural networks