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Penalized Variable Selection for Joint AFT Random-Effect Model With Clustered Competing-Risks Data.

Lin Hao1, Il Do Ha2

  • 1College of Economics Management, Weifang University of Science and Technology, Shouguang, China.

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Summary
This summary is machine-generated.

This study introduces a new variable selection method for clustered competing-risks data using penalized h-likelihood. Simulation results show penalized methods like SCAD and HL outperform LASSO for accurate clinical trial analysis.

Keywords:
AFT random‐effect modelH‐likelihoodclustered competing risks datacompeting risks modelspenalized variable selection

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • Clustered competing-risks data are common in multi-center clinical trials.
  • Event occurrences within clusters complicate analysis, requiring methods that account for correlations.
  • Traditional hazard-based models are often used, but survival time analysis offers interpretability.

Purpose of the Study:

  • To propose a variable selection method for fixed effects in joint accelerated failure time (AFT) models for clustered competing-risks data.
  • To evaluate the performance of penalized h-likelihood (HL) procedures for variable selection in this context.
  • To compare penalized methods (SCAD, HL) against LASSO for improved model accuracy.

Main Methods:

  • Developed a variable selection technique using a penalized h-likelihood (HL) approach.
  • Applied the method within a cause-specific joint AFT random-effect modeling framework.
  • Conducted simulation studies to assess the proposed method's efficacy.

Main Results:

  • The penalized HL procedure demonstrated effective variable selection for fixed effects.
  • Simulation studies indicated that penalized methods, specifically SCAD and HL, are more suitable than LASSO.
  • The proposed method was successfully illustrated using two real-world clinical datasets.

Conclusions:

  • The penalized h-likelihood approach provides a robust method for variable selection in joint AFT models for clustered competing-risks data.
  • Penalized methods (SCAD, HL) offer superior performance compared to LASSO in this setting.
  • The developed technique is applicable to real clinical data analysis, enhancing interpretability.