title: Spatial extended hazard model with application to prostate cancer survival.
abstract: This article develops a Bayesian semiparametric approach to the extended hazard model, with generalization to high-dimensional spatially grouped data. County-level spatial correlation is accommodated marginally through the normal transformation model of Li and Lin (2006, Journal of the American Statistical Association 101, 591-603), using a correlation structure implied by an intrinsic conditionally autoregressive prior. Efficient Markov chain Monte Carlo algorithms are developed, especially applicable to fitting very large, highly censored areal survival data sets. Per-variable tests for proportional hazards, accelerated failure time, and accelerated hazards are efficiently carried out with and without spatial correlation through Bayes factors. The resulting reduced, interpretable spatial models can fit significantly better than a standard additive Cox model with spatial frailties.
authors.affiliation: Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, U.S.A.
authors.forename: Li
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, South Carolina, U.S.A.
authors.forename: Timothy
authors.initials: T
authors.lastname: Hanson
authors.affiliation: Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, South Carolina, U.S.A.
authors.forename: Jiajia
authors.initials: J
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000401:mortality*; D012042:Registries; D013022:South Carolina / Q000453:epidemiology; D016019:Survival Analysis
keywords: Censored data; Gaussian Copula; Intrinsic autoregressive prior; Normal transformation model