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High-dimensional partially linear functional Cox models.

Xin Chen1,2, Hua Liu3, Jiaqi Men4

  • 1School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China.

Biometrics
|January 14, 2025
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Summary
This summary is machine-generated.

This study introduces a new high-dimensional partially linear functional Cox model to analyze survival data with non-linear functional predictor effects. The model improves accuracy for time-to-event analysis in scenarios like kidney transplant survival.

Keywords:
B-splinefunctional principal component analysislong-term survival analysisnon-linear effect

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Functional Data Analysis

Background:

  • Traditional functional Cox models assume linear relationships between functional principal component (FPC) scores and hazard rates.
  • This linear assumption is often violated in real-world applications, such as analyzing kidney transplant recipient survival times.
  • Existing methods may not adequately capture complex, non-linear influences of functional predictors on time-to-event outcomes.

Purpose of the Study:

  • To develop and validate a high-dimensional partially linear functional Cox model.
  • To accommodate non-linear effects of functional predictors in time-to-event data analysis.
  • To improve survival time predictions by incorporating both functional and scalar predictors.

Main Methods:

  • Introduction of a high-dimensional partially linear functional Cox model.
  • Application of the group smoothly clipped absolute deviation (SCAD) method for variable selection of scalar predictors and FPCs.
  • Utilizing B-splines for estimating non-linear effects of functional predictors.

Main Results:

  • Simulation studies demonstrate the finite sample performance of the proposed model's estimates.
  • The model successfully identifies significant scalar predictors associated with long-term survival in kidney transplant recipients.
  • Inferences are made regarding the non-linear effects of functional predictors on patient hazard rates.

Conclusions:

  • The proposed high-dimensional partially linear functional Cox model effectively handles non-linear functional predictor effects in survival analysis.
  • This approach offers a more flexible and accurate alternative to traditional functional Cox models.
  • The model provides valuable insights into factors influencing survival time, particularly in complex medical datasets like kidney transplantation.