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，用于评估灵活形状的癌症集群，称为集群形态分析(CMA)。到目前为止，癌症地理研究的两个主要缺陷是，它们经常假设集群具有特定的形状(例如圆形或椭圆形)，并且没有使用地理位置、高危人群、人口统计学、协变量和观察到的癌症病例数量来评估统计能力。这个项目克服了这些限制。将使用一套不同大小、相对风险和形状的貌似合理的集群对11种集群技术进行功率分析。然后根据统计能力和假阳性比例对结果进行排名，理由是基于集群的癌症监测的目标应该是(1)找到真实的集群，同时(2)避免虚假集群。然后，CMA综合那些被发现具有最佳统计性能的聚类方法的结果。这一方法被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，这三个县构成了一个重要的集群，从1950年到现在一直持续并增长。与只采用一种形状且仅依赖一种聚类方法的聚类方法相比，CMA是一个显著的进步。主要的创新是创建了用于分析癌症发病率和死亡率数据的方法和软件，以准确地识别由癌症风险过剩的地理亚群定义的灵活形状的集群。与公共健康相关：该项目的技术和软件将通过(1)基于灵活的形状而不是基于任意形状的“模板”(如圆形和椭圆形)对聚集物进行准确检测；(2)针对软件用户仔细研究的特定地理位置、癌症和亚群对聚集性技术的统计能力进行自动评估；以及(3)聚集性形态分析，其综合不同聚类法的结果以更准确地识别真正的聚集物。据我们所知，这个项目的技术和软件将第一次在一个单一的、全面的框架内解决所有这些因素。

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.