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

•We propose a L1/2 penalized accelerated failure time (AFT) model.•A coordinate descent algorithm with renewed L1/2 threshold is developed.•The L1/2 penalized AFT model is able to reduce the size of the predictor in practice.•The classifier based on the model is suitable for the high dimension biological data.

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 L1/2 regularization estimator with the accelerated failure time (AFT) model. The L1/2 regularization could be seen as a typical delegate of Lq(0