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Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review

  • Hyokyoung Grace Hong
  • Yi LiEmail author
Article

Abstract

Many modern biomedical studies have yielded survival data with high-throughput predictors. The goals of scientific research often lie in identifying predictive biomarkers, understanding biological mechanisms and making accurate and precise predictions. Variable screening is a crucial first step in achieving these goals. This work conducts a selective review of feature screening procedures for survival data with ultrahigh dimensional covariates. We present the main methodologies, along with the key conditions that ensure sure screening properties. The practical utility of these methods is examined via extensive simulations. We conclude the review with some future opportunities in this field.

Keywords

survival analysis ultrahigh dimensional predictors variable screening sure screening property 

MR Subject Classification

97K80 

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Notes

Acknowledgements

We thank Dr. Jialiang Li for providing the code for the survival impact index screening and Ms. Martina Fu for proofreading the manuscript.

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Copyright information

© Editorial Committee of Applied Mathematics-A Journal of Chinese Universities and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Statistics and ProbabilityMichigan State UniversityEast LansingUSA
  2. 2.Department of BiostatisticsUniversity of MichiganAnn ArborUSA

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