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