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Feature screening in ultrahigh-dimensional varying-coefficient Cox model.

Guangren Yang1, Ling Zhang2, Runze Li3

  • 1Department of Statistics, School of Economics, Jinan University, Guangzhou, China 510632.

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Summary
This summary is machine-generated.

This study introduces a new feature screening method for varying-coefficient Cox models with many covariates. The proposed joint partial likelihood approach effectively identifies relevant predictors in ultrahigh-dimensional survival analysis.

Keywords:
Cox modelPartial likelihoodPenalized likelihoodUltrahigh-dimensional survival data

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Varying-coefficient Cox models are essential for analyzing time-to-event data with dynamic covariate effects.
  • Ultrahigh-dimensional data presents challenges for traditional statistical methods in survival analysis.
  • Existing marginal screening methods may not fully capture complex covariate interactions.

Purpose of the Study:

  • To develop a novel feature screening procedure for varying-coefficient Cox models in ultrahigh-dimensional settings.
  • To address the limitations of marginal screening by utilizing joint information from all predictors.
  • To ensure the proposed method possesses the sure screening property, guaranteeing inclusion of relevant variables.

Main Methods:

  • A feature screening procedure based on the joint partial likelihood of all predictors.
  • Development of an effective algorithm with proven ascent properties.
  • Theoretical proof of the sure screening property for the proposed method.

Main Results:

  • The proposed joint partial likelihood screening procedure demonstrates the sure screening property.
  • Simulations confirm the effectiveness of the new method in finite samples.
  • Comparison with marginal screening procedures highlights the advantages of the joint approach.

Conclusions:

  • The developed feature screening method is effective for varying-coefficient Cox models in ultrahigh-dimensional data.
  • The joint partial likelihood approach offers an improvement over marginal screening techniques.
  • The procedure is validated through simulations and a genomic data illustration.