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Hierarchical Bayesian spatiotemporal analysis of revascularization odds using smoothing splines
Author(s) -
Silva Giovani L.,
Dean C. B.,
Niyonsenga Théophile,
Vanasse Alain
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3094
Subject(s) - markov chain monte carlo , odds , bayesian probability , random effects model , smoothing , bayesian inference , statistics , computer science , inference , econometrics , markov chain , mathematics , artificial intelligence , logistic regression , medicine , meta analysis
Hierarchical Bayesian models are proposed for over‐dispersed longitudinal spatially correlated binomial data. This class of models accounts for correlation among regions by using random effects and allows a flexible modelling of spatiotemporal odds by using smoothing splines. The aim is (i) to develop models which will identify temporal trends of odds and produce smoothed maps including regional effects, (ii) to specify Markov chain Monte Carlo (MCMC) inference for fitting such models, (iii) to study the sensitivity of such Bayesian binomial spline spatiotemporal analyses to prior assumptions, and (iv) to compare mechanisms for assessing goodness of fit. An analysis of regional variation for revascularization odds of patients hospitalized for acute coronary syndrome in Quebec motivates and illustrates the methods developed. Copyright © 2007 John Wiley & Sons, Ltd.