Covariate selection for accelerated failure time data

ABSTRACT

Selection of appropriate predictors for right censored time to event data is very often encountered by the practitioners. We consider the ℓ₁ penalized regression or “least absolute shrinkage and selection operator” as a tool for predictor selection in association with accelerated failure time model. The choice of the penalizing parameter λ is crucial to identify the correct set of covariates. In this paper, we propose an information theory-based method to choose λ under log-normal distribution. Furthermore, an efficient algorithm is discussed in the same context. The performance of the proposed λ and the algorithm is illustrated through simulation studies and a real data analysis. The convergence of the algorithm is also discussed.

KEYWORDS:

• Accelerated failure time model
• Bhattacharya distance
• index of resolvability
• Kullback–Leibler measure
• Shannon's entropy
• ℓ₁ penalty.

MATHEMATICS SUBJECT CLASSIFICATION:

• 62Nxx