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.
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We thank Dr. Jialiang Li for providing the code for the survival impact index screening and Ms. Martina Fu for proofreading the manuscript.
Supported by the National Natural Science Foundation of China (11528102) and the National Institutes of Health (U01CA209414).
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Hong, H.G., Li, Y. Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review. Appl. Math. J. Chin. Univ. 32, 379–396 (2017). https://doi.org/10.1007/s11766-017-3547-8
- survival analysis
- ultrahigh dimensional predictors
- variable screening
- sure screening property
MR Subject Classification