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Quantile forward regression for high-dimensional survival data.

Eun Ryung Lee1, Seyoung Park1, Sang Kyu Lee2,3

  • 1Department of Statistics, Sungkyunkwan University, Seoul, 03063, Korea.

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|July 2, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel quantile forward regression model for high-dimensional survival data, offering personalized risk predictions beyond average outcomes. The method ensures accurate variable selection for tailored health insights.

Keywords:
BICCensored dataHigh dimensionModel selectionQuantile regression

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Existing prediction models often focus on average outcomes, failing to capture individual variations.
  • Covariate effects can differ across the entire outcome distribution, necessitating quantile-specific analysis.

Purpose of the Study:

  • To develop a flexible, high-dimensional survival data model that accounts for individual characteristics.
  • To propose a quantile forward regression model for personalized risk prediction.

Main Methods:

  • Utilizing quantile forward regression for high-dimensional survival data.
  • Employing asymmetric Laplace distribution (ALD) maximization for variable selection.
  • Applying extended Bayesian Information Criterion (EBIC) for final model derivation.

Main Results:

  • The proposed method demonstrates sure screening property and selection consistency.
  • Application to a national health survey dataset highlights the benefits of quantile-specific prediction.

Conclusions:

  • The quantile forward regression model provides a more accurate and flexible approach to risk prediction.
  • This method enhances personalized medicine by considering individual-specific covariate effects.