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Feature screening in ultrahigh-dimensional additive Cox model.

Guangren Yang1, Sumin Hou1, Luheng Wang2

  • 1Department of Statistics, School of Economics, Jinan University, Guangzhou, People's Republic of China.

Journal of Statistical Computation and Simulation
|October 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a feature screening method for additive Cox models with many covariates. The procedure effectively identifies important predictors, ensuring accurate survival analysis model building.

Keywords:
The additive Cox modelpartial likelihoodspline approximationsultrahigh-dimensional survival data

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Area of Science:

  • Statistics
  • Survival Analysis
  • Machine Learning

Background:

  • The additive Cox model is valuable for analyzing time-to-event data with dynamic covariate effects.
  • Ultrahigh-dimensional data presents challenges for traditional statistical modeling techniques.

Purpose of the Study:

  • To develop an effective feature screening procedure for additive Cox models in ultrahigh-dimensional settings.
  • To ensure the proposed method reliably identifies relevant predictors for survival analysis.

Main Methods:

  • A novel feature screening procedure is proposed for the additive Cox model.
  • An effective algorithm with proven ascent properties is developed to implement the procedure.
  • Theoretical guarantees, including the sure screening property, are established.

Main Results:

  • The screening procedure demonstrates high probability of including all true active predictors.
  • The proposed algorithm is computationally efficient and stable.
  • Monte Carlo simulations confirm the procedure's strong finite sample performance.

Conclusions:

  • The proposed feature screening method is effective for additive Cox models with ultrahigh-dimensional covariates.
  • The method provides a reliable way to identify significant predictors, enhancing survival model interpretability.
  • The approach is validated through simulation studies and a real-world data application.