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Few Bayesian methods for analyzing high-dimensional sparse survival dataprovide scalable variable selection, effect estimation and uncertaintyquantification. Such methods often either sacrifice uncertainty quantificationby computing maximum a posteriori estimates, or quantify the uncertainty athigh (unscalable) computational expense. We bridge this gap and develop aninterpretable and scalable Bayesian proportional hazards model for predictionand variable selection, referred to as SVB. Our method, based on a mean-fieldvariational approximation, overcomes the high computational cost of MCMC whilstretaining useful features, providing a posterior distribution for theparameters and offering a natural mechanism for variable selection viaposterior inclusion probabilities. The performance of our proposed method isassessed via extensive simulations and compared against other state-of-the-artBayesian variable selection methods, demonstrating comparable or betterperformance. Finally, we demonstrate how the proposed method can be used forvariable selection on two transcriptomic datasets with censored survivaloutcomes, and how the uncertainty quantification offered by our method can beused to provide an interpretable assessment of patient risk.

Variational Bayes for high-dimensional proportional hazards models with applications within gene expression