Cluster morphology analysis.
Cluster morphology analysis.
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DOI：
10.1016/j.sste.2009.08.002
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发表时间：
2009-10
影响因子：
3.4
通讯作者：
Jacquez GM
中科院分区：
文献类型：
--
作者：
Jacquez GM
大多数疾病聚类方法假设特定的形状，并且不使用适用的地理，高危人群和协变量来评估统计功效。集群形态分析（CMA）假设不同相对风险和形状的集群，对替代技术进行功效分析。结果按统计功效和假阳性进行排名，其基本原理是监测应（1）发现真实聚类，同时（2）避免假聚类。CMA然后综合最强大的方法的结果。CMA在模拟研究中进行了评估，并应用于密歇根州的胰腺癌死亡率，发现了灵活形状的集群，同时定期评估统计功效。
Most disease clustering methods assume specific shapes and do not evaluate statistical power using the applicable geography, at-risk population, and covariates. Cluster Morphology Analysis (CMA) conducts power analyses of alternative techniques assuming clusters of different relative risks and shapes. Results are ranked by statistical power and false positives, under the rationale that surveillance should (1) find true clusters while (2) avoiding false clusters. CMA then synthesizes results of the most powerful methods. CMA was evaluated in simulation studies and applied to pancreatic cancer mortality in Michigan, and finds clusters of flexible shape while routinely evaluating statistical power.