A spatial scan statistic for ordinal data
A spatial scan statistic for ordinal data
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DOI：
10.1002/sim.2607
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发表时间：
2007-03-30
影响因子：
2
通讯作者：
Klassen, Ann C.
中科院分区：
文献类型：
--
作者：
Jung, Inkyung;Kulldorff, Martin;Klassen, Ann C.
空间扫描统计被广泛用于计数数据，以检测高或低发病率、死亡率或流行率的地理疾病集群，并评估其统计意义。然而，有些数据本质上是顺序的或连续的，因此有必要对数据进行二分法，以便对计数数据使用传统的扫描统计。于是，信息就会丢失，而截止点的选择往往是武断的。在本文中，我们提出了一种有序数据的空间扫描统计量，它允许我们在不做任何进一步假设的情况下分析包含有序结构的这类数据。检验统计量基于似然比检验，并使用蒙特卡罗假设检验进行评估。建议的方法是使用马里兰州癌症登记处的前列腺癌分级和分期数据进行说明的。通过模拟研究，检验了该检验的统计能力、敏感性和正预测值。版权所有(C)2006 John Wiley&Sons，Ltd.
Spatial scan statistics are widely used for count data to detect geographical disease clusters of high or low incidence, mortality or prevalence and to evaluate their statistical significance. Some data are ordinal or continuous in nature, however, so that it is necessary to dichotomize the data to use a traditional scan statistic for count data. There is then a loss of information and the choice of cut-off point is often arbitrary. In this paper, we propose a spatial scan statistic for ordinal data, which allows us to analyse such data incorporating the ordinal structure without making any further assumptions. The test statistic is based on a likelihood ratio test and evaluated using Monte Carlo hypothesis testing. The proposed method is illustrated using prostate cancer grade and stage data from the Maryland Cancer Registry. The statistical power, sensitivity and positive predicted value of the test are examined through a simulation study. Copyright (c) 2006 John Wiley & Sons, Ltd.