scholarly journals Partial Cox regression analysis for high-dimensional microarray gene expression data

2004 ◽  
Vol 20 (Suppl 1) ◽  
pp. i208-i215 ◽  
Author(s):  
H. Li ◽  
J. Gui
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Gene Expression Data ◽  
Cox Regression ◽  
Microarray Gene Expression Data ◽  
High Dimensional ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Cox Regression Analysis ◽  
Microarray Gene

Cross-validated Cox regression on microarray gene expression data

2006 ◽  
Vol 25 (18) ◽  
pp. 3201-3216 ◽  
Author(s):  
Hans C. van Houwelingen ◽  
Tako Bruinsma ◽  
Augustinus A. M. Hart ◽  
Laura J. van't Veer ◽  
Lodewyk F. A. Wessels
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Cox Regression ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Microarray Gene

Cox Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Qiang Zhao ◽  
Jianguo Sun
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Principal Component Regression ◽  
Predictive Ability ◽  
Principal Component ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
New Approach ◽  
Microarray Gene

Statistical analysis of microarray gene expression data has recently attracted a great deal of attention. One problem of interest is to relate genes to survival outcomes of patients with the purpose of building regression models for the prediction of future patients' survival based on their gene expression data. For this, several authors have discussed the use of the proportional hazards or Cox model after reducing the dimension of the gene expression data. This paper presents a new approach to conduct the Cox survival analysis of microarray gene expression data with the focus on models' predictive ability. The method modifies the correlation principal component regression (Sun, 1995) to handle the censoring problem of survival data. The results based on simulated data and a set of publicly available data on diffuse large B-cell lymphoma show that the proposed method works well in terms of models' robustness and predictive ability in comparison with some existing partial least squares approaches. Also, the new approach is simpler and easy to implement.

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