Hierarchical Bayesian spatiotemporal analysis of revascularization odds using smoothing splines

Giovani L. Silva, C. B. Dean, Théophile Niyonsenga, Alain Vanasse

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2381-2401
Number of pages21
JournalStatistics in Medicine
Issue number13
Publication statusPublished - 15 Jun 2008
Externally publishedYes


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