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A New Method For Covariate Selection In Cox Model

Author

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  • Das Ujjwal

    (Operations Management, Quantitative Methods and Information Systems Area, Indian Institute of Management, Udaipur, 313001, Rajasthan, India)

  • Ebrahimi Nader

    (Division of Statistics, Northern Illinois University, Dekalb, IL, 60115, United States)

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.

Suggested Citation

  • Das Ujjwal & Ebrahimi Nader, 2018. "A New Method For Covariate Selection In Cox Model," Statistics in Transition New Series, Statistics Poland, vol. 19(2), pages 297-314, June.
    • Handle: RePEc:vrs:stintr:v:19:y:2018:i:2:p:297-314:n:4
    DOI: 10.21307/stattrans-2018-017
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    References listed on IDEAS

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    1. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    2. Ujjwal Das & Nader Ebrahimi, 2017. "Covariate selection for accelerated failure time data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(8), pages 4051-4064, April.
    3. Nader Ebrahimi & Ehsan S. Soofi & Refik Soyer, 2010. "Information Measures in Perspective," International Statistical Review, International Statistical Institute, vol. 78(3), pages 383-412, December.
    4. Peter D. Grünwald, 2007. "The Minimum Description Length Principle," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262072815, April.
    5. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
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