Cox Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression
Date
2007-05-29
Authors
Zhao, Qiang
Sun, Jianguo
Journal Title
Journal ISSN
Volume Title
Publisher
The Berkeley Electronic Press
Abstract
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.
Description
Keywords
survival analysis, cox model, microarray gene expression data, correlation principal, component regression, Mathematics
Citation
Zhao, Q., & Sun, J. (2007). Cox survival analysis of microarray gene expression data using correlation principal component regression. Statistical Applications in Genetics and Molecular Biology, 6(1).
Rights
Rights Holder
© 2007 The Berkeley Electronic Press.