Bayesian parametric accelerated failure time spatial model and its application to prostate cancer
Prostate cancer (PrCA) is the most common cancer diagnosed in American men and the second leading cause of death from malignancies. There are large geographical variation and racial disparities existing in the survival rate of PrCA. Much work on the spatial survival model is based on
the proportional hazards (PH) model, but few focused on the accelerated failure time (AFT) model. In this paper, we investigate the PrCA data of Louisiana from the Surveillance, Epidemiology, and End Results program and the violation of the PH assumption suggests that the spatial survival
model based on the AFT model is more appropriate for this data set. To account for the possible extra-variation, we consider spatially referenced independent or dependent spatial structures. The deviance information criterion is used to select a best-fitting model within the Bayesian frame
work. The results from our study indicate that age, race, stage, and geographical distribution are significant in evaluating PrCA survival.
Keywords: Bayesian; accelerated failure time model; deviance information criterion; likelihood; spatial
Document Type: Research Article
Affiliations: 1: Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA 2: Division of Biostatistics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA
Publication date: 01 March 2011
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