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Updated: Jul 8, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Parametric models for spatially correlated survival data for individuals with multiple cancers
Ulysses Diva1, Dipak K Dey, Sudipto Banerjee
1Global Biometric Sciences, Bristol-Myers Squibb Company, Wallingford, CT, U.S.A.
Statistics in Medicine
|January 3, 2008
Summary
This study introduces Bayesian hierarchical survival models to analyze spatial patterns in cancer data. The spatial proportional hazards model is recommended for understanding how multiple gastrointestinal cancers affect patients in Iowa.
Area of Science:
- Biostatistics
- Spatial Epidemiology
- Cancer Research
Background:
- Survival data analysis can be improved by incorporating spatial variation.
- Jointly modeling time-to-event data for multiple diseases in the same patient offers insights into disease co-occurrence.
Purpose of the Study:
- To propose Bayesian hierarchical survival models for analyzing spatial correlations in time-to-event data.
- To capture spatial correlations within proportional hazards (PH) and proportional odds (PO) frameworks.
- To apply these models to multiple gastrointestinal cancer data from Iowa.
Main Methods:
- Bayesian hierarchical survival models were developed using parametric baseline distributions (Weibull for PH, log-logistic for PO).
- Spatial correlation was introduced via county-cancer-level frailties.
- Models were applied to Surveillance Epidemiology and End Results (SEER) data for gastrointestinal cancers in Iowa.
Main Results:
- Model checking and comparison were performed among the developed models.
- Implementation issues were identified and discussed.
- The spatial proportional hazards (PH) model was found to be suitable for the analyzed dataset.
Conclusions:
- Bayesian hierarchical survival models effectively incorporate spatial variation and joint disease modeling.
- The spatial PH model is recommended for analyzing spatial correlations in multi-cancer survival data, as demonstrated with Iowa gastrointestinal cancer data.
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