A New method for covariate selection in Cox model

Ujjwal  Das; Nader  Ebrahimi

Ujjwal Das Operations Management, Quantitative Methods and Information Systems Area, Indian Institute of Management, Udaipur 313001, Rajasthan, India. , Nader Ebrahimi Division of Statistics, Northern Illinois University, Dekalb, IL 60115, USA. Statistics in Transition new series, vol. 19, 2018, 2, pages: 297-314 Published online: 1 June 2018

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ABSTRACT

In a wide spectrum of natural and social sciences, very often one encounters a large number of predictors for time to event data. An important task is to select right ones, and thereafter carry out the analysis. The l₁ penalized regression, known as “least absolute shrinkage and selection operator" (LASSO) became a popular approach for predictor selection in last two decades. The LASSO regression involves a penalizing parameter (commonly denoted by λ) which controls the extent of penalty and hence plays a crucial role in identifying the right covariates. In this paper we propose an information theory-based method to determine the value of λ in association with the Cox proportional hazards model. Furthermore, an efficient algorithm is discussed in the same context. We demonstrate the usefulness of our method through an extensive simulation study. We compare the performance of our proposal with existing methods. Finally, the proposed method and the algorithm are illustrated using a real data set.

KEYWORDS

Bhattacharya distance, index of resolvability, Kullback-Leibler measure, l1 penalty, proportional hazards model, time to event data.

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