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• W2892344515 abstract "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 random effects. We apply the models to examine prostate cancer data from the Surveillance, Epidemiology, and End Results (SEER) registry, which displays marked spatial variation." @default.
• W2892344515 created "2018-09-27" @default.
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• W2892344515 date "2018-12-01" @default.
• W2892344515 modified "2023-10-18" @default.
• W2892344515 title "Bayesian cure-rate survival model with spatially structured censoring" @default.
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• W2892344515 doi "https://doi.org/10.1016/j.spasta.2018.08.007" @default.
• W2892344515 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7449293" @default.
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