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Activity Number: 368
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #318690 View Presentation
Title: Bayesian Cure Rate Survival Model with Spatially Structured Censoring
Author(s): Georgiana Onicescu* and Andrew B. Lawson
Companies: and Medical University of South Carolina
Keywords: spatial ; Bayesian ; prostate ; MCMC ; survival ; cure rate

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.

Authors who are presenting talks have a * after their name.

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