Regularized estimation in the accelerated failure time model with high-dimensional covariates.
We consider two regularization approaches, the LASSO and the threshold-gradient-directed regularization, for estimation and variable selection in the accelerated failure time model with multiple covariates based on Stute's weighted least squares method. The Stute estimator uses Kaplan-Meier weights to account for censoring in the least squares criterion. The weighted least squares objective function makes the adaptation of this approach to multiple covariate settings computationally feasible. We use V-fold cross-validation and a modified Akaike's Information Criterion for tuning parameter selection, and a bootstrap approach for variance estimation. The proposed method is evaluated using simulations and demonstrated on a real data example.
Huang
Jian
J
Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa 52242, USA. jian@stat.uiowa.edu
Ma
Shuangge
S
Xie
Huiliang
H
D000465:Algorithms; D000704:Analysis of Variance; D001699:Biometry / Q000379:methods*; D016018:Least-Squares Analysis; D016014:Linear Models; D008103:Liver Cirrhosis / Q000097:blood / Q000401:mortality; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors