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

arXiv: https://arxiv.org/abs/2112.10270 · HTML: https://mlsanity.com/paper/2112.10270/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 68 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Advances in Variational Inference](https://mlsanity.com/paper/1711.05597/) (2017)
- [Variational Bayes for high-dimensional linear regression with sparse priors](https://mlsanity.com/paper/1904.07150/) (2019)
- [Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence](https://mlsanity.com/paper/2106.15980/) (2021)
- [Lasso Meets Horseshoe : A Survey](https://mlsanity.com/paper/1706.10179/) (2017)
- [Spike and slab variational Bayes for high dimensional logistic regression](https://mlsanity.com/paper/2010.11665/) (2020)
- [Group Factor Analysis](https://mlsanity.com/paper/1411.5799/) (2014)
- [Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO](https://mlsanity.com/paper/2010.06451/) (2020)
- [Spike and slab Bayesian sparse principal component analysis](https://mlsanity.com/paper/2102.00305/) (2021)
- [Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data](https://mlsanity.com/paper/1604.04280/) (2016)

## Built on by (most cited first, 3 of 3)

- [Logistic Variational Bayes Revisited](https://mlsanity.com/paper/2406.00713/) (2024)
- [Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification](https://mlsanity.com/paper/2503.13078/) (2025)
- [Robust and Scalable Variational Bayes](https://mlsanity.com/paper/2504.12528/) (2025)

## Abstract (arXiv)

Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often either sacrifice uncertainty quantification by computing maximum a posteriori estimates, or quantify the uncertainty at high (unscalable) computational expense. We bridge this gap and develop an interpretable and scalable Bayesian proportional hazards model for prediction and variable selection, referred to as SVB. Our method, based on a mean-field variational approximation, overcomes the high computational cost of MCMC whilst retaining useful features, providing a posterior distribution for the parameters and offering a natural mechanism for variable selection via posterior inclusion probabilities. The performance of our proposed method is assessed via extensive simulations and compared against other state-of-the-art Bayesian variable selection methods, demonstrating comparable or better performance. Finally, we demonstrate how the proposed method can be used for variable selection on two transcriptomic datasets with censored survival outcomes, and how the uncertainty quantification offered by our method can be used to provide an interpretable assessment of patient risk.