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Regularized Weighted Nonparametric Likelihood Approach for High-Dimension Sparse Subdistribution Hazards Model for

Leili Tapak1,2, Michael R Kosorok3, Majid Sadeghifar4

  • 1Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.

Computational and Mathematical Methods in Medicine
|September 30, 2021
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This study introduces a new penalized regression method for high-dimensional competing risk data, improving variable selection accuracy. The minimax concave penalty (MCP) demonstrated superior performance in identifying relevant genetic markers for bladder cancer prognosis.

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Area of Science:

  • Biostatistics
  • Genomics
  • Computational Biology

Background:

  • High-dimensional data, particularly omics data, are crucial in biomedical research for understanding disease dynamics and patient prognosis.
  • Analyzing time-to-event data with competing risks in high dimensions necessitates specialized statistical modeling techniques.
  • Existing variable selection methods for competing risks, often based on partial likelihood, have limitations in high-dimensional settings.

Purpose of the Study:

  • To extend weighted likelihood-based penalized approaches for direct variable selection in high-dimensional competing risk data using subdistribution hazards models.
  • To accommodate semiparametric regression models with potential time-varying effects, relaxing the proportional hazards assumption.
  • To evaluate the performance of various penalties, including minimax concave penalty (MCP), adaptive LASSO, and smoothly clipped absolute deviation (SCAD), in terms of sensitivity and specificity.

Main Methods:

  • Development of a weighted likelihood-based penalized approach for variable selection under the subdistribution hazards model.
  • Extension to a broader class of semiparametric models, allowing for time-varying effects.
  • Simulation studies comparing MCP, adaptive LASSO, SCAD, and their L2 counterparts using sensitivity and specificity metrics.

Main Results:

  • The minimax concave penalty (MCP) and its L2 counterpart (MCP-L2) showed superior performance by selecting fewer non-informative variables compared to other penalties.
  • Sensitivity across all investigated penalties was comparable.
  • Application to bladder cancer genomic data identified six genes (CDC20, NCF2, SMARCAD1, RTN4, ETFDH, SON) significantly correlated with the subdistribution.

Conclusions:

  • The proposed weighted likelihood-based penalized method offers an effective approach for variable selection in high-dimensional competing risk settings.
  • MCP and MCP-L2 penalties are recommended for their efficiency in identifying relevant variables while minimizing noise.
  • The identified genes provide potential biomarkers for bladder cancer prognosis and therapeutic strategies.