Bayesian Variable Selection For Survival Data Using Inverse Moment Priors

TL;DR

This paper introduces a Bayesian variable selection method for survival data in high-dimensional genomic studies, utilizing inverse moment priors and stochastic search, implemented in R package BVSNLP, showing improved performance over existing methods.

Contribution

The paper presents a novel Bayesian variable selection approach with inverse moment priors for Cox models, enhancing selection consistency and computational efficiency in high-dimensional survival analysis.

Findings

Outperforms existing variable selection methods in simulations.

Provides more consistent gene selection in genomic datasets.

Enables efficient computation with parallel processing in R.

Abstract

Efficient variable selection in high-dimensional cancer genomic studies is critical for discovering genes associated with specific cancer types and for predicting response to treatment. Censored survival data is prevalent in such studies. In this article we introduce a Bayesian variable selection procedure that uses a mixture prior composed of a point mass at zero and an inverse moment prior in conjunction with the partial likelihood defined by the Cox proportional hazard model. The procedure is implemented in the R package BVSNLP, which supports parallel computing and uses a stochastic search method to explore the model space. Bayesian model averaging is used for prediction. The proposed algorithm provides better performance than other variable selection procedures in simulation studies, and appears to provide more consistent variable selection when applied to actual genomic datasets.