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Abstract:
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We propose a Bayesian spatial model for time-to event data in which we allow the censoring mechanism to depend on covariates and have a spatial structure. The survival model incorporates a cure rate fraction and assumes that the time to event follows a Weibull distribution, with covariates such as race, stage, grade, marital status and age at diagnosis being linked to its scale parameter. With right censoring being a primary concern, we consider a joint logistic regression model for the death (versus censoring) indicator, allowing dependence on covariates and including a spatial structure via the use of uncorrelated and correlated random effects. We apply the model to examine prostate cancer data from the Surveillance, Epidemiology, and End Results (SEER) registry, which has a marked spatial variation.
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