High-dimensional survival analyses require calibrated risk and measurable uncertainty, but standard elastic net Cox models provide only point estimates. We develop a Bayesian elastic net Cox (BEN–Cox) model for high-dimensional proportional hazards regression that places a hierarchical global–local shrinkage prior on coefficients and performs full Bayesian inference via Hamiltonian Monte Carlo. We represent the elastic net penalty as a global–local Gaussian scale mixture with hyperpriors that learn the ℓ1/ℓ2 trade-off, enabling adaptive sparsity that preserves correlated gene groups; using HMC with the Cox partial likelihood, we obtain full posterior distributions for hazard ratios and patient-level survival curves. Methodologically, we formalize a Bayesian analogue of the elastic net grouping effect at the posterior mode and establish posterior contraction under sparsity for the Cox partial likelihood, supporting the stability of the resulting risk scores. On the METABRIC breast cancer cohort (n=1903; p=440 gene-level features after preprocessing, derived from an Illumina HT-12 array with ≈24,000 probes at the raw feature level), BEN–Cox achieves slightly lower prediction error, higher discrimination, and better global calibration than a tuned ridge Cox, lasso Cox, and elastic net Cox baselines on a held-out test set. Posterior summaries provide credible intervals for hazard ratios and identify a compact gene panel that remains biologically plausible. BEN–Cox provides an uncertainty-aware alternative to tuned penalized Cox models with theoretical support, offering modest improvements in calibration and providing an interpretable sparse signature in highly-correlated survival data.
Yılmaz et al. (Fri,) studied this question.