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
Accepted author manuscript, 404 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -