Cancer Cluster Morphology

描述（由申请人提供）：该项目将开发一种新的元分析方法来评估柔性形状的癌症簇，称为簇形态分析（CMA）。迄今为止，癌症地理研究的两个主要缺陷是，它们通常假设集群具有特定的形状（例如圆形或椭圆形），并且不使用地理、高危人群、人口统计学、协变量和正在调查的癌症观察病例数来评估统计效力。这个项目克服了这些限制。将对11种聚类技术进行功率分析，使用一套不同大小、相对风险和形状的似是而非的聚类。然后根据统计能力和假阳性的比例对结果进行排序，基于簇的癌症监测的目标应该是(1)发现真实的簇，(2)避免假簇。然后，CMA综合那些具有最佳统计性能的聚类方法的结果。该方法应用于密歇根州的胰腺癌发病率和死亡率，重点关注三个县，这三个县组成了一个重要的集群，从1950年到现在一直持续增长。相对于只假设一种形状并只依赖一种聚类方法的聚类方法，CMA是一个重大的进步。主要的创新是创造了分析癌症发病率和死亡率数据的方法和软件，以准确识别由癌症风险过高的地理亚人群定义的灵活形状的集群。公共卫生相关性：该项目的技术和软件将通过以下方式提供更简洁和准确的癌症集群描述：(1)准确检测基于灵活形状的集群，而不是基于任意形状的“模板”，如圆形和椭圆；(2)软件用户对特定地理、癌症和亚人群的聚类技术统计能力的自动评估；(3)聚类形态分析，综合各种聚类方法的结果，更准确地识别真实的聚类。据我们所知，这个项目的技术和软件将是第一个在一个单一的、全面的框架内解决所有这些因素的。

DESCRIPTION (provided by applicant): This project will develop a new, meta-analytic approach for evaluating cancer clusters of flexible shape called Cluster Morphology Analysis (CMA). To date, two of the major deficiencies of geographic studies of cancer are that they often assume clusters have a specific shape (e.g. circle or ellipse) and do not evaluate statistical power using the geography, at-risk population, demographics, covariates and numbers of observed cases of the cancer under investigation. These limitations are overcome by this project. Power analyses will be conducted for 11 clustering techniques using a suite of plausible clusters of different sizes, relative risks and shapes. The results are then ranked by statistical power and by the proportion of false positives, under the rationale that the objective of cluster-based cancer surveillance should be to (1) find true clusters while (2) avoiding false clusters. CMA then synthesizes the results of those clustering methods found to have the best statistical performance. This approach is applied to pancreatic cancer incidence and mortality in Michigan, focusing on three counties that comprise a significant cluster that persists and grows from 1950 to the present day. CMA is a significant advance over clustering approaches that assume just one shape and rely on only one clustering method. The major innovation is the creation of methods and software for analyzing cancer incidence and mortality data to accurately identify flexibly shaped clusters defined by geographic sub-population of excess cancer risk. PUBLIC HEALTH RELEVANCE: The techniques and software from this project will provide a more concise and accurate description of cancer clusters via (1) the accurate detection of clusters founded on flexible shapes, rather than on arbitrary shape "templates" such as circles and ellipses; (2) the automated evaluation of the statistical power of clustering techniques for the specific geography, cancer and sub-population being scrutinized by the software user; and (3) Cluster Morphology Analysis that synthesizes results across clustering approaches to more accurately identify true clusters. To our knowledge the techniques and software from this project will be the first to address all of these factors within a single, comprehensive framework.