Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models

2016 ◽  
Vol 35 (15) ◽  
pp. 2561-2573 ◽  
Author(s):  
Nils Ternès ◽  
Federico Rotolo ◽  
Stefan Michiels
Keyword(s):  
False Discovery Rate ◽  
Regression Models ◽  
Cox Regression ◽  
High Dimensional ◽  
Lasso Penalty ◽  
False Discovery

scholarly journals Ensembling Variable Selectors by Stability Selection for the Cox Model

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Qing-Yan Yin ◽  
Jun-Li Li ◽  
Chun-Xia Zhang
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
Cox Model ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Real World Data ◽  
Stability Selection ◽  
False Discovery ◽  
Selection For ◽  
Base Learner

As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs) have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010), a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR) and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

scholarly journals Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Shaima Belhechmi ◽  
Riccardo De Bin ◽  
Federico Rotolo ◽  
Stefan Michiels
Keyword(s):  
Regression Models ◽  
Cox Regression ◽  
Predictor Variables ◽  
High Dimensional
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