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Bayesian Elastic Net Cox Models for Time-to-Event Prediction: Application to a Breast Cancer Cohort.

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Summary
This summary is machine-generated.

We introduce a Bayesian elastic net Cox (BEN-Cox) model for high-dimensional survival data. BEN-Cox offers uncertainty quantification and improved risk prediction over standard penalized Cox models.

Keywords:
Bayesian elastic netCox proportional hazardshigh-dimensional survivalposterior contractionshrinkage estimator

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Genomics

Background:

  • High-dimensional survival analysis requires accurate risk prediction and uncertainty estimation.
  • Standard elastic net Cox models provide only point estimates, limiting uncertainty assessment.

Purpose of the Study:

  • Develop a Bayesian elastic net Cox (BEN-Cox) model for high-dimensional proportional hazards regression.
  • Provide calibrated risk estimates and measurable uncertainty for survival data.

Main Methods:

  • Implemented a hierarchical global-local shrinkage prior on coefficients.
  • Utilized Hamiltonian Monte Carlo (HMC) for full Bayesian inference.
  • Represented the elastic net penalty using a global-local Gaussian scale mixture with hyperpriors.

Main Results:

  • BEN-Cox achieved lower prediction error, higher discrimination, and better global calibration on the METABRIC breast cancer cohort.
  • Obtained full posterior distributions for hazard ratios and patient-level survival curves.
  • Identified a biologically plausible sparse gene panel.

Conclusions:

  • BEN-Cox offers an uncertainty-aware alternative to standard penalized Cox models.
  • Provides theoretical support and modest improvements in calibration.
  • Generates interpretable sparse signatures in highly-correlated survival data.