Group Selection in Semiparametric Accelerated Failure Time Model

Abstract

In survival analysis, a number of regression models can be used to estimate the effects of covariates on the censored survival outcome. When covariates can be naturally grouped, group selection is important in these models. Motivated by the group bridge approach for variable selection in a multiple linear regression model, we consider group selection in a semiparametric accelerated failure time (AFT) model using Stute’s weighted least squares and a group bridge penalty. This method is able to simultaneously carry out feature selection at both the group and within-group individual variable levels, and enjoys the powerful oracle group selection property. Simulation studies indicate that the group bridge approach for the AFT model can correctly identify important groups and variables even with high censoring rate. A real data analysis is provided to illustrate the application of the proposed method.