This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time-and space-varying covariate e ects. Data analysis is implemented via Markov chain Monte Carlo methods. The methodology is illustrated by a tentativ
โฆ LIBER โฆ
Bayesian modelling of inseparable space-time variation in disease risk
โ Scribed by Leonhard Knorr-Held
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 219 KB
- Volume
- 19
- Category
- Article
- ISSN
- 0277-6715
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Generalized relative and absolute risk models, in which various functions of time and age modify the excess relative or absolute risk of radiation-induced cancer, are fitted to the Japanese atomic bomb survivor cancer incidence data set. Among generalized relative risk models, those in which a produ