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Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)

机译:集成嵌套拉普拉斯近似(INLA)的空间疾病率贝叶斯扫描

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Among the many tools suited to detect local clusters in group-level data, Kulldorff-Nagarwalla's spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799-810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319-392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff-Nagarwalla's statistic.
机译:在许多适合检测组级别数据中本地聚类的工具中,Kulldorff-Nagarwalla的空间扫描统计数据获得了广泛的欢迎(Kulldorff和Nagarwalla在Stat Med 14(8):799-810,1995)。使统计推断可行所需的基本假设非常强大,因为假定空间单位中的计数是独立的Poisson分布随机变量。不幸的是,空间单位中的结果通常不是彼此独立的,并且彼此接近的区域的风险估计将趋于正相关,因为它们具有许多空间变化的特征。因此,我们介绍了一种基于贝叶斯模型的聚类检测算法,该算法在存在空间自相关风险的情况下进行。最近基于集成嵌套拉普拉斯逼近的新数值方法的发展使我们的方法成为可能,通过该方法,我们可以在较短的计算时间内直接计算后边际的非常精确的逼近(Rue等人,JRSS B 71(2): 319-392,2009)。仿真数据和案例研究表明,我们方法的性能至少可以与Kulldorff-Nagarwalla的统计数据相媲美。

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