Multivariate parametric spatiotemporal models for county level breast cancer survival data
Xiaoping Jin1, Bradley P Carlin
1Division of Biostatistics, School of Public Health, University of Minnesota, Mayo Mail Code 303, Minneapolis, Minnesota 55455-0392, USA.
Lifetime Data Analysis
|March 8, 2005
Summary
This study introduces a new spatiotemporal model to analyze disease survival data, effectively capturing geographic and temporal correlations. The model improves understanding of regional disease patterns using breast cancer data.
Area of Science:
- Biostatistics
- Spatial Epidemiology
- Survival Analysis
Background:
- Geographic clustering necessitates spatial models in survival analysis.
- Existing models may not fully capture complex spatial and temporal correlations.
Purpose of the Study:
- To propose a multivariate conditionally autoregressive (MCAR) model for spatiotemporal survival data.
- To extend the MCAR model for temporal cohort effects.
- To efficiently fit the spatiotemporal model using a hierarchical Bayesian framework and Markov chain Monte Carlo (MCMC).
Main Methods:
- Developed a multivariate conditionally autoregressive (MCAR) model.
- Extended the model to incorporate temporal cohort effects.
- Implemented a hierarchical Bayesian framework with MCMC for model fitting.
Main Results:
- Successfully fitted a spatiotemporal model to county-level breast cancer data from Iowa.
- Demonstrated the model's ability to capture spatial and temporal correlations.
- Model comparison using Deviance Information Criterion (DIC) and spatial maps highlighted the approach's benefits.
Conclusions:
- The proposed spatiotemporal MCAR model effectively analyzes clustered survival data with geographic and temporal components.
- This approach offers enhanced insights into regional disease patterns and cohort effects.
- The methodology is robust and applicable to various public health surveillance datasets.
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