Singular Information for Cancer Cluster Detection

描述(由申请人提供)： 当遇到罕见的癌症，如喉癌或儿童白血病时，评估是否出现了病例聚集性往往是有意义的。通常情况下，零发病率的大片区域被小范围的病例聚集所点缀。需要能够检测某些癌症的聚集性，因为这些聚集性可能导致关于疾病的一般病因和疾病的局部环境风险因素的重要信息。在癌症监测中，由于察觉到与某些工商业过程或活动(如废物处理、废水扩散、农药扩散、移动通信广播)有关的居住环境风险，聚集性的检测变得重要。令人担忧的疾病结果类型多种多样，从呼吸系统癌症，如肺癌或喉癌，到儿童白血病、非霍奇金淋巴瘤和软组织肉瘤。最近对监测生物恐怖主义的兴趣也进一步加强了检测集群的适当方法的必要性，在生物恐怖主义中，集群可能是存在攻击的重要线索。 这项研究的目的是：1)发展和评估多水平半参数模型中的奇异信息方法，用于稀疏癌症数据的表面相对风险估计；2)检验不同分布假设的随机效应；3)将该方法应用于小区域癌症研究中的集群检测。 多水平半参数模型被构建为包括与整个感兴趣区域的嵌套分区相关联的多组随机效果，这使得在较小部分的较大区域内测试异常利率具有很大的灵活性。检验偏离无异常比率的零值等同于检验一个或多个等于零的方差分量，并将其作为一个奇异信息问题来处理。并研究了置信度区间的构造。假设随机效应服从多种不同的分布，如重尾分布、偏态分布和双峰分布。预期的结果是一种基于可能性的方法，在比以前可能的更广泛的应用中拟合稳健的线性混合模型。

DESCRIPTION (provided by applicant): When rare cancers, such as larynx or childhood leukemia, are encountered it is often of interest to assess whether clustering of cases has arisen. Typically large areas of zero-incidence are punctuated with small aggregations of cases. There is a need to be able to detect clusters of certain cancers as these can lead to important information concerning general etiology of the disease and localized environmental risk factors for the disease. In cancer surveillance the detection of clustering has become important due to the perceived residential environmental risks relating to certain industrial/commercial processes or activities (such as waste disposal, effluent dispersal, pesticide dispersal, mobile communication broadcasting). The types of disease outcome of concern have varied from respiratory cancers such as lung or larynx, to childhood leukemia, non-Hodgkin's lymphoma, and soft tissue sarcomas. The need for appropriate methods of detection of clusters is also further strengthened by the recent interest in surveillance for bioterrorism where clustering could be a vital clue to the existence of an attack. The study is aimed at 1) developing and evaluating singular information methods in multilevel semiparametric models for surface estimation of relative risk with sparse cancer data, 2) testing different distributional assumptions for random effects and 3) applying the methods to clusters detection in small area cancer studies. Multilevel semiparametric models are constructed to include multiple sets of random effects associated with nested partitions of the entire territory of interest that allow great flexibility for testing unusual rates within smaller parts of larger areas. Testing departures from the null value of no unusual rates is equivalent to testing the one or multiple variance components equal to zero and is approached as a singular information problem. Construction of confidence intervals is also studied. The random effects are assumed to follow a variety of different distributions, such as heavy-tailed, skewed and bimodal distributions. The expected result is a likelihood-based approach to fitting robust linear mixed models in a wider range of applications than was previously possible.