The L1/2 regularization approach for survival analysis in the accelerated failure time model

The analysis of high-dimensional and low-sample size microarray data for survival analysis of cancer patients is an important problem. It is a huge challenge to select the significantly relevant bio-marks from microarray gene expression datasets, in which the number of genes is far more than the size of samples. In this article, we develop a robust prediction approach for survival time of patient by a L-1/2 regularization estimator with the accelerated failure time (AFT) model. The L-1/2 regularization could be seen as a typical delegate of L-q(0 < q < 1) regularization methods and it has shown many attractive features. In order to optimize the problem of the relevant gene selection in high-dimensional biological data, we implemented the L-1/2 regularized AFT model by the coordinate descent algorithm with a renewed half thresholding operator. The results of the simulation experiment showed that we could obtain more accurate and sparse predictor for survival analysis by the L-1/2 regularized AFT model compared with other L-1 type regularization methods. The proposed procedures are applied to five real DNA microarray datasets to efficiently predict the survival time of patient based on a set of clinical prognostic factors and gene signatures. (C) 2014 Elsevier Ltd. All rights reserved.