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

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., Appalac...

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Bibliographic Details
Main Authors: Ming Wang, Zheng Li, Jun Lu, Lijun Zhang, Yimei Li, Liangliang Zhang
Format: Article
Language:English
Published: BMC 2024-04-01
Series:BMC Medical Research Methodology
Subjects:
Accelerated failure times
Bayesian inference
Monte Carlo Markov chain
Multivariate conditional autoregressive priors
Prostate cancer
Spatial-temporal modeling
Online Access:https://doi.org/10.1186/s12874-024-02201-w

Internet

https://doi.org/10.1186/s12874-024-02201-w

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