Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression Qiang Zhao Mathematics Department Texas State University, San Marcos 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.
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