Age–period–cohort models and disease mapping
- 27 June 2003
- journal article
- research article
- Published by Wiley in Environmetrics
- Vol. 14 (5) , 475-490
- https://doi.org/10.1002/env.600
Abstract
Joint modelling of space and time variation of the risk of disease is an important topic in descriptive epidemiology. Most of the proposals in this field deal with at most two time scales (age–period or age–cohort). We propose a hierarchical Bayesian model that can be used as a general framework to jointly study the evolution in time and the spatial pattern of the risk of disease. The rates are modelled as a function of purely spatial terms (local effects of risk factors that do not vary in time), time effects (on the three time axes: age, calendar period and birth cohort) and space–time interactions that describe area specific time patterns. Copyright © 2003 John Wiley & Sons, Ltd.Keywords
This publication has 30 references indexed in Scilit:
- Bayesian Measures of Model Complexity and FitJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- Hierarchical Spatio-Temporal Mapping of Disease RatesJournal of the American Statistical Association, 1997
- Bayes FactorsJournal of the American Statistical Association, 1995
- Bayesian analysis of survival on multiple time scalesStatistics in Medicine, 1994
- Inference from Iterative Simulation Using Multiple SequencesStatistical Science, 1992
- Local Model InfluenceJournal of the American Statistical Association, 1989
- Models for temporal variation in cancer rates. II: Age–period–cohort modelsStatistics in Medicine, 1987
- Age, period and cohort models applied to cancer mortality ratesStatistics in Medicine, 1982