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.

译文

:提出层次贝叶斯模型用于过度分散的纵向空间相关二项式数据。此类模型通过使用随机效应来说明区域之间的相关性,并可以通过使用平滑样条线对时空几率进行灵活的建模。目的是(i)开发能够识别赔率的时间趋势并产生包括区域效应在内的平滑图的模型;(ii)指定马尔可夫链蒙特卡罗(MCMC)推论来拟合此类模型;(iii)研究模型的敏感性。这样的贝叶斯二项式样条时空分析到先前的假设,以及(iv)比较评估拟合优度的机制。对魁北克因急性冠脉综合征住院的患者进行血运重建几率的区域变化进行分析,可以激发并说明所开发的方法。

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