Covariate selection for accelerated failure time data

Ujjwal Das and Nader Ebrahimi

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 8, 4051-4064

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.

Date: 2017
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DOI: 10.1080/03610926.2015.1078475

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