Detection of temporal changes in the spatial distribution of cancer rates using local Moran's I and geostatistically simulated spatial neutral models.
Detection of temporal changes in the spatial distribution of cancer rates using local Moran's I and geostatistically simulated spatial neutral models.
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DOI：
10.1007/s10109-005-0154-7
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发表时间：
2005-05-01
影响因子：
2.9
通讯作者：
Jacquez, Geoffrey M
中科院分区：
文献类型：
--
作者：
Goovaerts, Pierre;Jacquez, Geoffrey M
本文提出了空间相关中性模型的首次应用，使用局部 Moran's I 统计来检测跨空间和时间的死亡率变化。序贯高斯模拟用于在日益严格的条件下实现死亡率的空间分布：1）样本直方图的再现，2）根据数据建模的空间自相关模式的再现，3）合并通过对观察到的死亡率进行地统计平滑获得的区域背景，以及4）合并在先前时间间隔观察到的平滑区域背景。然后使用 Morany's I 统计量的两个新的时空变体对模拟的中性模型进行处理，这使得人们能够识别死亡率超出过去空间模式的显着变化。最后，使用根据数据的时空性质定制的聚类/离群值的原始分类来显示结果。使用这种新方法，对美国所有州经济区 (SEA) 记录的宫颈癌死亡率的时空分布进行了探索，分为 9 个时间段，每个时间段为 5 年。空间自相关的结合导致显着的 SEA 单位少于传统空间独立假设下获得的单位，这证实了早期的主张，即当使用独立假设的测试应用于空间相关数据时，I 类错误可能会增加。将区域背景整合到中性模型中会产生截然不同的空间集群和异常值，突出显示在恒定风险的原假设下应用局部 Moran's I 时模糊的局部模式。
This paper presents the first application of spatially correlated neutral models to the detection of changes in mortality rates across space and time using the local Moran's I statistic. Sequential Gaussian simulation is used to generate realizations of the spatial distribution of mortality rates under increasingly stringent conditions: 1) reproduction of the sample histogram, 2) reproduction of the pattern of spatial autocorrelation modeled from the data, 3) incorporation of regional background obtained by geostatistical smoothing of observed mortality rates, and 4) incorporation of smooth regional background observed at a prior time interval. The simulated neutral models are then processed using two new spatio-temporal variants of the Morany's I statistic, which allow one to identify significant changes in mortality rates above and beyond past spatial patterns. Last, the results are displayed using an original classification of clusters/outliers tailored to the space-time nature of the data. Using this new methodology the space-time distribution of cervix cancer mortality rates recorded over all US State Economic Areas (SEA) is explored for 9 time periods of 5 years each. Incorporation of spatial autocorrelation leads to fewer significant SEA units than obtained under the traditional assumption of spatial independence, confirming earlier claims that Type I errors may increase when tests using the assumption of independence are applied to spatially correlated data. Integration of regional background into the neutral models yields substantially different spatial clusters and outliers, highlighting local patterns which were blurred when local Moran's I was applied under the null hypothesis of constant risk.