Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival. (February 2017)

• Record Type: Journal Article
• Authors: Newcombe, PJ; Raza Ali, H; Blows, FM; Provenzano, E; Pharoah, PD; Caldas, C; Richardson, S
• Is Part Of: Statistical methods in medical research. Volume 26:Number 1(2017)
• Page Start: 414
• Page End: 436
• Publication Date: 2017-02
• Subjects: survival analysis -- Bayesian variable selection -- reversible jump -- stability selection -- breast cancer -- gene expression -- penalised regression -- MCMC; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280214548748
• Languages: English
• ISSNs: 0962-2802

Abstract

As data-rich medical datasets are becoming routinely collected, there is a growing demand for regression methodology that facilitates variable selection over a large number of predictors. Bayesian variable selection algorithms offer an attractive solution, whereby a sparsity inducing prior allows inclusion of sets of predictors simultaneously, leading to adjusted effect estimates and inference of which covariates are most important. We present a new implementation of Bayesian variable selection, based on a Reversible Jump MCMC algorithm, for survival analysis under the Weibull regression model. A realistic simulation study is presented comparing against an alternative LASSO-based variable selection strategy in datasets of up to 20, 000 covariates. Across half the scenarios, our new method achieved identical sensitivity and specificity to the LASSO strategy, and a marginal improvement otherwise. Runtimes were comparable for both approaches, taking approximately a day for 20, 000 covariates. Subsequently, we present a real data application in which 119 protein-based markers are explored for association with breast cancer survival in a case cohort of 2287 patients with oestrogen receptor-positive disease. Evidence was found for three independent prognostic tumour markers of survival, one of which is novel. Our new approach demonstrated the best specificity.