Spike-and-slab type variable selection in the Cox proportional hazards model for high-dimensional features

• Published in: Journal of applied statistics Vol. 49; no. 9; pp. 2189 - 2207
• Main Authors: Wu, Ryan, Ahn, Mihye, Yang, Hojin
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 04.07.2022; Taylor & Francis Ltd
• Subjects: Bayesian modeling; latent indicator; lung adenocarcinoma; Lung cancer; Markov chain Monte Carlo; Performance evaluation; score function; Statistical methods; Statistical models; stochastic variable search; Survival
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2021.1893285

In this paper, we develop a variable selection framework with the spike-and-slab prior distribution via the hazard function of the Cox model. Specifically, we consider the transformation of the score and information functions for the partial likelihood function evaluated at the given data from the parameter space into the space generated by the logarithm of the hazard ratio. Thereby, we reduce the nonlinear complexity of the estimation equation for the Cox model and allow the utilization of a wider variety of stable variable selection methods. Then, we use a stochastic variable search Gibbs sampling approach via the spike-and-slab prior distribution to obtain the sparsity structure of the covariates associated with the survival outcome. Additionally, we conduct numerical simulations to evaluate the finite-sample performance of our proposed method. Finally, we apply this novel framework on lung adenocarcinoma data to find important genes associated with decreased survival in subjects with the disease.