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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Biopharmaceutical Statistics on 26/10/2017, available online: http://www.tandfonline.com/10.1080/10543406.2017.1378662

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Model selection based on combined penalties for biomarker identification

Research output: Contribution to journalJournal articlepeer-review

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Model selection based on combined penalties for biomarker identification. / Vradi, Eleni; Brannath, Werner; Jaki, Thomas; Vonk, Richardus.

In: Journal of Biopharmaceutical Statistics, Vol. 28, No. 4, 04.07.2018, p. 735-749.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Vradi, E, Brannath, W, Jaki, T & Vonk, R 2018, 'Model selection based on combined penalties for biomarker identification', Journal of Biopharmaceutical Statistics, vol. 28, no. 4, pp. 735-749. https://doi.org/10.1080/10543406.2017.1378662

APA

Vradi, E., Brannath, W., Jaki, T., & Vonk, R. (2018). Model selection based on combined penalties for biomarker identification. Journal of Biopharmaceutical Statistics, 28(4), 735-749. https://doi.org/10.1080/10543406.2017.1378662

Vancouver

Vradi E, Brannath W, Jaki T, Vonk R. Model selection based on combined penalties for biomarker identification. Journal of Biopharmaceutical Statistics. 2018 Jul 4;28(4):735-749. https://doi.org/10.1080/10543406.2017.1378662

Author

Vradi, Eleni ; Brannath, Werner ; Jaki, Thomas ; Vonk, Richardus. / Model selection based on combined penalties for biomarker identification. In: Journal of Biopharmaceutical Statistics. 2018 ; Vol. 28, No. 4. pp. 735-749.

Bibtex

@article{f2d0727b5277468ca22e3d7901d1df13,
title = "Model selection based on combined penalties for biomarker identification",
abstract = "The growing role of targeted medicine has led to an increased focus on the development of actionable biomarkers. Current penalized selection methods that are used to identify biomarker panels for classification in high-dimensional data, however, often result in highly complex panels that need careful pruning for practical use. In the framework of regularization methods, a penalty that is a weighted sum of the L1 and L0 norm has been proposed to account for the complexity of the resulting model. In practice, the limitation of this penalty is that the objective function is non-convex, non-smooth, the optimization is computationally intensive and the application to high-dimensional settings is challenging. In this paper, we propose a stepwise forward variable selection method which combines the L0 with L1 or L2 norms. The penalized likelihood criterion that is used in the stepwise selection procedure results in more parsimonious models, keeping only the most relevant features. Simulation results and a real application show that our approach exhibits a comparable performance with common selection methods with respect to the prediction performance while minimizing the number of variables in the selected model resulting in a more parsimonious model as desired.",
keywords = "Biomarker panels, combined penalties, model selection, penalized regression, regularization, sparsity, stepwise variable selection, treatment responder",
author = "Eleni Vradi and Werner Brannath and Thomas Jaki and Richardus Vonk",
note = "This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Biopharmaceutical Statistics on 26/10/2017, available online: http://www.tandfonline.com/10.1080/10543406.2017.1378662",
year = "2018",
month = jul,
day = "4",
doi = "10.1080/10543406.2017.1378662",
language = "English",
volume = "28",
pages = "735--749",
journal = "Journal of Biopharmaceutical Statistics",
issn = "1054-3406",
publisher = "Taylor and Francis Ltd.",
number = "4",

}

RIS

TY - JOUR

T1 - Model selection based on combined penalties for biomarker identification

AU - Vradi, Eleni

AU - Brannath, Werner

AU - Jaki, Thomas

AU - Vonk, Richardus

N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Biopharmaceutical Statistics on 26/10/2017, available online: http://www.tandfonline.com/10.1080/10543406.2017.1378662

PY - 2018/7/4

Y1 - 2018/7/4

N2 - The growing role of targeted medicine has led to an increased focus on the development of actionable biomarkers. Current penalized selection methods that are used to identify biomarker panels for classification in high-dimensional data, however, often result in highly complex panels that need careful pruning for practical use. In the framework of regularization methods, a penalty that is a weighted sum of the L1 and L0 norm has been proposed to account for the complexity of the resulting model. In practice, the limitation of this penalty is that the objective function is non-convex, non-smooth, the optimization is computationally intensive and the application to high-dimensional settings is challenging. In this paper, we propose a stepwise forward variable selection method which combines the L0 with L1 or L2 norms. The penalized likelihood criterion that is used in the stepwise selection procedure results in more parsimonious models, keeping only the most relevant features. Simulation results and a real application show that our approach exhibits a comparable performance with common selection methods with respect to the prediction performance while minimizing the number of variables in the selected model resulting in a more parsimonious model as desired.

AB - The growing role of targeted medicine has led to an increased focus on the development of actionable biomarkers. Current penalized selection methods that are used to identify biomarker panels for classification in high-dimensional data, however, often result in highly complex panels that need careful pruning for practical use. In the framework of regularization methods, a penalty that is a weighted sum of the L1 and L0 norm has been proposed to account for the complexity of the resulting model. In practice, the limitation of this penalty is that the objective function is non-convex, non-smooth, the optimization is computationally intensive and the application to high-dimensional settings is challenging. In this paper, we propose a stepwise forward variable selection method which combines the L0 with L1 or L2 norms. The penalized likelihood criterion that is used in the stepwise selection procedure results in more parsimonious models, keeping only the most relevant features. Simulation results and a real application show that our approach exhibits a comparable performance with common selection methods with respect to the prediction performance while minimizing the number of variables in the selected model resulting in a more parsimonious model as desired.

KW - Biomarker panels

KW - combined penalties

KW - model selection

KW - penalized regression

KW - regularization

KW - sparsity

KW - stepwise variable selection

KW - treatment responder

U2 - 10.1080/10543406.2017.1378662

DO - 10.1080/10543406.2017.1378662

M3 - Journal article

AN - SCOPUS:85032352019

VL - 28

SP - 735

EP - 749

JO - Journal of Biopharmaceutical Statistics

JF - Journal of Biopharmaceutical Statistics

SN - 1054-3406

IS - 4

ER -