The Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data forUS individuals diagnosed with cancer. Semiparametric Bayesian methods are computationallyexpensive to fit for such large ...The Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data forUS individuals diagnosed with cancer. Semiparametric Bayesian methods are computationallyexpensive to fit for such large data-sets. This paper develops a cost-effective Markov chain MonteCarlo strategy for censored outcomes to fit a semiparametric bayesian analysis of SEER data ofNew Mexico. We use an accelerated failure time model, with Dirichlet process random effectsfor inter-subject variation, and intrinsic conditionally autoregressive random effects for spatialcorrelations. The results offer insights into differences in breast cancer mortality rates betweenethnic groups, tumor grade and spatial effect of counties.展开更多
基金National Science Foundation[grant number DMS-1461948].
文摘The Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data forUS individuals diagnosed with cancer. Semiparametric Bayesian methods are computationallyexpensive to fit for such large data-sets. This paper develops a cost-effective Markov chain MonteCarlo strategy for censored outcomes to fit a semiparametric bayesian analysis of SEER data ofNew Mexico. We use an accelerated failure time model, with Dirichlet process random effectsfor inter-subject variation, and intrinsic conditionally autoregressive random effects for spatialcorrelations. The results offer insights into differences in breast cancer mortality rates betweenethnic groups, tumor grade and spatial effect of counties.
