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#5740. Penalized Cox’s proportional hazards model for high-dimensional survival data with grouped predictors
| June 2026 | publication date |
| Proposal available till | 11-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Statistics and Probability; Statistics, Probability and Uncertainty; Computational Theory and Mathematics; Theoretical Computer Science; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №5740.1   | Contract №5740.2 | Contract №5740.3 | Contract №5740.4 |
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Abstract:
The rapid development of next-generation sequencing technologies has made it possible to measure the expression profiles of thousands of genes simultaneously. Often, there exist group structures among genes manifesting biological pathways and functional relationships. Analyzing such high-dimensional and structural datasets can be computationally expensive and results in the complicated models that are hard to interpret. To address this, variable selection such as penalized methods are often taken. Here, we focus on the Cox’s proportional hazards model to deal with censoring data. Most of the existing penalized methods for Cox’s model are the group lasso methods that show deficiencies, including the over-shrinkage problem. In addition, the contemporary algorithms either exhibit the loss of efficiency or require the group-wise orthonormality assumption. Hence, efficient algorithms for general design matrices are needed to enable practical applications. In this paper, we investigate and comprehensively evaluate three group penalized methods for Cox’s model: the group lasso and two nonconvex penalization methods—group SCAD and group MCP—that have several advantages over the group lasso. These methods are able to perform group selection in both non-overlapping and overlapping cases.
Keywords:
Group-wise descent; High-dimensional; Majorization-minimization (MM); Penalized method; Survival analysis
Contacts :
2 place - free (for sale)
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4 place - free (for sale)
Abstract:
The rapid development of next-generation sequencing technologies has made it possible to measure the expression profiles of thousands of genes simultaneously. Often, there exist group structures among genes manifesting biological pathways and functional relationships. Analyzing such high-dimensional and structural datasets can be computationally expensive and results in the complicated models that are hard to interpret. To address this, variable selection such as penalized methods are often taken. Here, we focus on the Cox’s proportional hazards model to deal with censoring data. Most of the existing penalized methods for Cox’s model are the group lasso methods that show deficiencies, including the over-shrinkage problem. In addition, the contemporary algorithms either exhibit the loss of efficiency or require the group-wise orthonormality assumption. Hence, efficient algorithms for general design matrices are needed to enable practical applications. In this paper, we investigate and comprehensively evaluate three group penalized methods for Cox’s model: the group lasso and two nonconvex penalization methods—group SCAD and group MCP—that have several advantages over the group lasso. These methods are able to perform group selection in both non-overlapping and overlapping cases.
Keywords:
Group-wise descent; High-dimensional; Majorization-minimization (MM); Penalized method; Survival analysis
Contacts :
