Bayesian Variable Selection for Cox Regression Model with Spatially Varying Coefficients with Applications to Louisiana Respiratory Cancer Data

• Activity Number: 254 - Contributed Poster Presentations: Section on Bayesian Statistical Science
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Jinjian Mu* and Guanyu Hu and Qingyang Liu and Lynn Kuo
• Companies: University of Connecticut and University of Connecticut and University of Connecticut and University of Connecticut
• Keywords: Horseshoe prior; Spatial survival; SEER data; MCMC

Abstract

The Cox regression model is a commonly used model in survival analysis. In public health studies, clinical data are often collected from medical service providers of differ- ent locations. There are large geographical variations in the survival rates from cancer. In this paper, we focus on the variable selection issue for Cox regression model with spatially varying coefficients. We propose a Bayesian hierarchical model for variable selection where horseshoe prior and point mass mixture prior are employed for sparsity and determining whether a covariate is spatially varying. An efficient two-stage computational method is used for posterior inference and variable selection. It essentially applies the existing method for maximizing partial likelihood for the Cox model by site independently first, and then we develop an MCMC algorithm for variable selection based on the results of the first stage. Extensive simulation studies based on this method are carried out to examine the empirical performance of the proposed method. Finally, we apply the proposed methodology to analyzing a real data set on respiratory cancer in Louisiana from the SEER program.