Copula Models for Spatial Epidemiology of Cancer

癌症空间流行病学的 Copula 模型

• 批准号: 8701837
• 负责人: John Hughes
• 金额: $ 7.6万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8701837
• 关键词: Address; Archives; Area; Atlas of Cancer Mortality in the United States; Bayesian Analysis; Binomial Model; Cervical; Characteristics; Communities; Computer software; Data; Data Analyses; Data Set; Dependence; Disease; Disease model; Documentation; Ecology; Epidemiologist; Four-dimensional; Geographic Information Systems; Geography; Health; Health Professional; Human Papillomavirus; Incidence; Malignant Neoplasms; Malignant Vaginal Neoplasm; Malignant neoplasm of anus; Malignant neoplasm of penis; Maps; Marketing; Measures; Medical Surveillance; Modeling; Performance; Play; Prevalence; Probability; Public Health; Role; Running; SEER Program; Staging; Statistical Methods; Statistical Models; Structure; Time; Variant; Vulva; base; cancer epidemiology; cancer risk; flexibility; malignant oropharynx neoplasm; novel strategies; public health relevance; response; simulation; user friendly software

DESCRIPTION (provided by applicant): A relatively new approach to spatial modeling is the copula-based approach, which has thus far been applied only to geostatistical data, i.e., data observed over a continuous spatial domain. The chief advantage of copula-based modeling is modularity. The dependence structure and the marginal distributions can be modeled separately and then joined by way of the probability integral transform. This approach can, in principle, allow copula-based areal models to overcome many of the problems associated with the most commonly used areal models. Specifically, we expect copula-based areal models (1) to be flexible and intuitive, (2) to permit positive spatial dependence for all types of data, (3) to be ow dimensional, (4) to permit efficient computation (so that large datasets can be handled), and (5) to provide reliable spatial regression inference (because spatial confounding is impossible). In this project, we will develop various copula models for areal data, and we will develop approaches to frequentist and Bayesian inference for the models. The performance of the copula models will be compared to one another and to existing areal models by way of an extensive simulation study and application to current SEER data for HPV-related cancers. We will also implement R software for fitting the copula models to data. The new routines (and thorough documentation) will be added to existing R package ngspatial, which is freely available from the Comprehensive R Archive Network.

说明(申请人提供)：一种相对较新的空间建模方法是基于Copula的方法，迄今仅适用于地质统计数据，即在连续空间域上观察到的数据。基于Copula的建模的主要优势是模块化。可以将相依结构和边缘分布分开建模，然后通过概率积分变换将其连接起来。原则上，这种方法可以允许基于Copula的面状模型克服与最常用的面状模型相关的许多问题。具体地说，我们期望基于Copula的面积模型(1)灵活和直观，(2)允许所有类型的数据具有正的空间相关性，(3)低维，(4)允许高效计算(以便可以处理大型数据集)，以及(5)提供可靠的空间回归推理(因为空间混淆是不可能的)。在这个项目中，我们将开发各种面状数据的Copula模型，并将为这些模型开发频率和贝叶斯推理方法。Copula模型的性能将通过广泛的模拟研究和应用于当前HPV相关癌症的SEER数据来相互比较，并与现有的区域模型进行比较。我们还将实施R软件，以将Copula模型与数据进行拟合。新的例行程序(和详尽的文件)将被添加到现有的R包ngspace中，该包可从全面R档案网免费获得。