title: Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model.
abstract: In this paper, we extend the spatially explicit survival model for small area cancer data by allowing dependency between space and time and using accelerated failure time models. Spatial dependency is modeled directly in the definition of the survival, density, and hazard functions. The models are developed in the context of county level aggregated data. Two cases are considered: the first assumes that the spatial and temporal distributions are independent; the second allows for dependency between the spatial and temporal components. We apply the models to prostate cancer data from the Louisiana SEER cancer registry.
authors.affiliation: 1 Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA.
authors.forename: Georgiana
authors.initials: G
authors.lastname: Onicescu
authors.affiliation: 1 Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Lawson
authors.affiliation: 2 Department of Biostatistics and Epidemiology, Arnold School of Public Health, University of South Carolina, Columbia, USA.
authors.forename: Jiajia
authors.initials: J
authors.lastname: Zhang
authors.affiliation: 1 Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA.
authors.forename: Mulugeta
authors.initials: M
authors.lastname: Gebregziabher
authors.affiliation: 1 Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA.
authors.forename: Kristin
authors.initials: K
authors.lastname: Wallace
authors.affiliation: 2 Department of Biostatistics and Epidemiology, Arnold School of Public Health, University of South Carolina, Columbia, USA.
authors.forename: Jan M
authors.initials: JM
authors.lastname: Eberth
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008145:Louisiana / Q000453:epidemiology; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D011471:Prostatic Neoplasms / Q000401:mortality; D012042:Registries; D018426:SEER Program; D062211:Spatio-Temporal Analysis; D016019:Survival Analysis; D013997:Time Factors
keywords: Accelerated failure time; Bayesian hierarchical models; Markov chain Monte Carlo; prostate cancer; spatial analysis