Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data.

ABSTRACT: Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consi

SUBMITTER: Wang M 

PROVIDER: S-EPMC11003030 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature