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

ABSTRACT:

Motivation

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.

Results

We bridge this gap and develop an interpretable and scalable Bayesian proportional hazards model for prediction and variable selection, referred to as sparse variational Bayes. Our method, based on a mean-field variational approximation, overcomes the high computational cost of Markov chain Monte Carlo, whilst retaining useful features, providing a posterior distribution for the parameters and offering a natural mechanism

SUBMITTER: Komodromos M 

PROVIDER: S-EPMC9364383 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature