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Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer
Ming Wang1, Zheng Li2, Jun Lu3
1Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA. mxw827@case.edu.
BMC Medical Research Methodology
|April 8, 2024
Summary
This study introduces advanced Bayesian survival models to analyze prostate cancer data, accounting for spatial and temporal variations. The findings help identify key risk factors and improve understanding of prostate cancer survival rates.
Area of Science:
- Biostatistics
- Epidemiology
- Cancer Research
Background:
- Prostate cancer exhibits significant geographical and racial disparities in incidence and mortality.
- The Cox proportional hazards model often fails due to violated proportional hazards assumptions in complex survival data.
- Accurate survival analysis is crucial for understanding prostate cancer trends and disparities.
Purpose of the Study:
- To develop and apply Bayesian accelerated failure time models for prostate cancer survival analysis.
- To incorporate spatial-temporal dependencies and relax the proportional hazards assumption.
- To identify significant risk factors influencing prostate cancer survival.
Main Methods:
- Bayesian accelerated failure time models with multivariate conditional autoregressive priors for spatial-temporal effects.
- Relaxation of the proportional hazards assumption and flexible frailty structures.
- Monte Carlo Markov chain (MCMC) for parameter estimation and deviance information criterion (DIC) for model selection.
Main Results:
- The proposed Bayesian models effectively handle spatial-temporal heterogeneity in prostate cancer survival.
- Identified significant risk factors associated with overall survival in the Pennsylvania cohort.
- Demonstrated the flexibility and robustness of the Bayesian approach compared to traditional models.
Conclusions:
- Bayesian accelerated failure time models provide a powerful framework for analyzing complex prostate cancer survival data.
- Accounting for spatial-temporal structures is essential for accurate epidemiological insights.
- The approach can inform public health strategies by identifying high-risk populations and factors.
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