论文标题
贝叶斯共享 - 限制空间扫描统计模型，用于事件时间数据
A Bayesian shared-frailty spatial scan statistic model for time-to-event data
论文作者
论文摘要
空间扫描统计是众所周知的，并且广泛使用了用于检测事件空间簇的方法。在事件时间数据的空间分析领域中，已经提出了几种扫描统计模型。但是，这些模型没有考虑到个体的潜在单位内空间相关性或空间单位之间的潜在相关性。为了克服这个问题，我们在这里提出了基于具有共同脆弱的Cox模型的扫描统计量，该模型考虑了空间单位之间的空间相关性。在仿真研究中，我们已经表明（i）（i）在存在空间内单位相关的情况下，实时数据的空间扫描统计模型无法维持I型误差，以及（ii）我们的模型在存在空间内单位相关和空间间单位相关性的情况下表现良好。我们的方法已应用于流行病学数据，并在法国北部末期肾脏疾病患者中检测死亡率的空间簇。
Spatial scan statistics are well known and widely used methods for the detection of spatial clusters of events. In the field of spatial analysis of time-to-event data, several models of scan statistics have been proposed. However, these models do not take into account the potential intra-unit spatial correlation of individuals nor a potential correlation between spatial units. To overcome this problem, we propose here a scan statistic based on a Cox model with shared frailty that takes into account the spatial correlation between spatial units. In simulation studies, we have shown that (i) classical models of spatial scan statistics for time-to-event data fail to maintain the type I error in the presence of intra-spatial unit correlation, and (ii) our model performs well in the presence of both intra-spatial unit correlation and inter-spatial unit correlation. Our method has been applied to epidemiological data and to the detection of spatial clusters of mortality in patients with end-stage renal disease in northern France.
