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Bayesian variable selection for a semi-competing risks model with three hazard functions.

Andrew G Chapple1, Marina Vannucci1,2, Peter F Thall2

  • 1Rice University, Department of Statistics, 6100 Main St., Duncan Hall 2124, Houston, TX 77005, U.S.A.

Computational Statistics & Data Analysis
|October 17, 2017
PubMed
Summary
This summary is machine-generated.

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This study introduces a new variable selection method for semi-competing risks, using spike-and-slab priors and a DIC-based threshold. Intensity-modulated radiation therapy significantly lowers risks of effusion and death in esophageal cancer patients.

Area of Science:

  • Biostatistics
  • Medical Statistics
  • Survival Analysis

Background:

  • Semi-competing risks models are crucial for analyzing time-to-event data with multiple event types.
  • Variable selection is essential for identifying significant predictors in complex regression models.
  • Existing methods may not adequately address the nuances of semi-competing risks with multiple hazards.

Purpose of the Study:

  • To develop a robust variable selection procedure for semi-competing risks regression models.
  • To propose a data-driven threshold selection rule using the Deviance Information Criterion (DIC).
  • To apply the developed method to esophageal cancer patient data for covariate identification.

Main Methods:

  • A semi-competing risks regression model with three hazard functions was utilized.
Keywords:
Metropolis-HastingsSemi-Competing RisksVariable Selection

Related Experiment Videos

  • Spike-and-slab priors and stochastic search variable selection algorithms were employed for posterior inference.
  • A novel threshold selection rule based on the DIC was devised and validated via simulation.

    • Main Results:

    • The proposed DIC-based thresholding rule effectively selected important covariates in a simulation study.
    • Application to esophageal cancer data identified key predictors for effusion, death before effusion, and death after effusion.
    • The method demonstrated robustness to hyperparameter choices, yielding consistent model selections.

    Conclusions:

    • The developed variable selection procedure offers a reliable approach for semi-competing risks analysis.
    • The DIC-based thresholding provides a principled way to select variables in this context.
    • Intensity-modulated radiation therapy is associated with significantly reduced risks of effusion and death in esophageal cancer patients.