Ranking based variable selection for censored data using AFT models

Abstract

Numerous variable selection techniques have been developed for complete high-dimensional data but very few of them for censored data. The techniques for complete data must be modified if censoring is present. In this paper, we consider the variable selection technique for accelerated failure time (AFT) models by extending the ranking-based variable selection (RBVS) algorithm and its iterative procedure as proposed in the work of Baranowski et al. through the Stute’s weighted least square technique. Simulation studies are conducted to demonstrate the performance of the proposed methods. We further illustrate the performance of this method with a mantle cell lymphoma microarray example. When there is no correlation among the covariates, the proposed method outperforms the iterative sure independence screening and stability selection methods in terms of overall performance for high-dimensional data. Real data analysis also suggests that the proposed method can be chosen for high-dimensional censored data analysis in parallel to other methods in the literature.

Keywords:

• Accelerated failure time
• High-dimensional data
• Stability selection
• Variable ranking
• Variable selection

Author contributions

MHRK supervised all tasks that were carried out by MA. Besides, MHRK designed and coordinated the statistical analysis and contributed to drafting the manuscript. MA carried out the study, particularly implemented the design, conducted statistical analysis and contributed to the writing of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The working data set used for this study has been submitted to the journal as additional supporting file.