Department of Biostatistics, Medical Informatics and Communication Technologies, Clermont University Hospital, Clermont-Ferrand F-63000, France.
Int J Health Geogr. 2013 Oct 25;12:47. doi: 10.1186/1476-072X-12-47.
Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region.
To explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff's spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region.
Consistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors.
The area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region.
传统的功效研究在评估聚类检测试验的性能方面能力有限。特别是，它们无法评估聚类位置的准确性，而这在这种评估中是至关重要的。此外，它们通常估计一个或几个特定替代假设的功效，因此无法评估整个区域的性能。高桥和坦戈提出了扩展功效的概念，该概念表示了零假设拒绝率和聚类位置的准确性。我们提出了一种系统的评估方法，使用扩展功效来生成一张图，显示整个区域内聚类检测试验的性能。
为了探索聚类检测试验在任何可能位置的相同聚类类型上的行为，我们连续应用了四个不同的空间和流行病学参数。这些参数确定了四个聚类集合，每个集合覆盖整个研究区域。我们为每个聚类模拟了 1000 个数据集，并使用 Kulldorff 的空间扫描统计进行了分析。从扩展功效曲线下的面积，我们为每个参数集构建了一张图，显示了整个区域的测试性能。
与之前的研究一致，空间扫描统计的性能随着疾病的基线发病率、风险人群的规模和聚类的强度（即相对风险）的增加而增加。然而，即使是非常相似的聚类（即，在上述因素方面相似），性能也是不均匀的，这表明其他因素的影响。
扩展功效曲线下的面积是性能的单一衡量标准，尽管需要进一步探索，但它适合对性能进行系统的空间评估。我们提出的性能图使流行病学家能够评估整个研究区域的聚类检测试验。
