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Singular Information for Cancer Cluster Detection

用于癌症簇检测的单一信息

基本信息

批准号：
6951807
负责人：
MATTEO BOTTAI
金额：
$ 7.28万
依托单位：
UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2004
资助国家：
美国
起止时间：
2004-09-22 至 2007-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6951807
关键词：
cancer risk clinical research health science research human data mathematical model model design /development neoplasm /cancer epidemiology orphan disease /drug statistics /biometry

项目摘要

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.

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，通常有兴趣评估是否出现了病例聚集。通常，大面积的零发病率地区会被小规模的病例聚集所打断。需要能够检测某些癌症的集群，因为这些可以导致关于疾病的一般病因学和疾病的局部环境风险因素的重要信息。在癌症监测中，由于感知到与某些工业/商业过程或活动（例如废物处理、污水散布、杀虫剂散布、移动的通信广播）有关的居住环境风险，集群检测变得重要。关注的疾病结局类型从呼吸道癌症（如肺或喉）到儿童白血病、非霍奇金淋巴瘤和软组织肉瘤不等。最近对监测生物恐怖主义的兴趣也进一步加强了对探测集群的适当方法的需要，因为集群可能是确定是否存在攻击的重要线索。本研究的目的是：1）开发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相对风险的表面估计，2）测试随机效应的不同分布假设，3）将该方法应用于小区域癌症研究中的聚类检测。多水平半参数模型的构建，包括多组随机效应与嵌套分区的整个领土的利益，允许很大的灵活性，测试不寻常的利率在较小的部分较大的地区。检验偏离零值的无异常率等价于检验一个或多个方差分量等于零，并作为一个奇异信息问题。还研究了置信区间的构造。假设随机效应遵循各种不同的分布，例如重尾分布、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。