Survival Regression Models With Dependent Bayesian Nonparametric Priors

Riva-Palacio, Alan; Leisen, Fabrizio; Griffin, Jim

Abstract

We present a novel Bayesian nonparametric model for regression in survival analysis. Our model builds on the classical neutral to the right model of Doksum and on the Cox proportional hazards model of Kim and Lee. The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates while allowing nonproportionality. The model can be seen as having competing latent risks. We characterize the posterior of the underlying dependent vector of completely random measures and study the asymptotic behavior of the model. We show how an MCMC scheme can provide Bayesian inference for posterior means and credible intervals. The method is illustrated using simulated and real data. Supplementary materials for this article are available online.

Citation

Riva-Palacio, A., Leisen, F., & Griffin, J. (2022). Survival Regression Models With Dependent Bayesian Nonparametric Priors. Journal of the American Statistical Association, 117(539), 1530-1539. https://doi.org/10.1080/01621459.2020.1864381

• Journal Article Type: Article
• Acceptance Date: Nov 22, 2020
• Online Publication Date: Feb 10, 2021
• Publication Date: 2022
• Deposit Date: Dec 19, 2020
• Publicly Available Date: Feb 11, 2022
• Journal: Journal of the American Statistical Association
• Print ISSN: 0162-1459
• Electronic ISSN: 1537-274X
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 117
• Issue: 539
• Pages: 1530-1539
• DOI: https://doi.org/10.1080/01621459.2020.1864381
• Keywords: Statistics, Probability and Uncertainty; Statistics and Probability
• Public URL: https://nottingham-repository.worktribe.com/output/5158108
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1864381
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 10 Feb 2021, available at: http://www.tandfonline.com/10.1080/01621459.2020.1864381