Hierarchical Bayesian spatiotemporal analysis of revascularization odds using smoothing splines

GL Silva, CB Dean, T Niyonsenga… - Statistics in …, 2008 - Wiley Online Library
Statistics in medicine, 2008Wiley Online Library
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 …
Abstract
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.
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