Hierachial Modeling Approaches for Geographical Boundary Analysis in Cancer Studi

癌症研究中地理边界分析的分层建模方法

• 批准号: 7097022
• 负责人: Sudipto Banerjee
• 金额: $ 24.61万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7097022
• 关键词: model; neoplasm /cancer

DESCRIPTION (provided by applicant): Boundary analysis concerns the detection and analysis of zones of abrupt change in spatial maps. Its importance in understanding scientific phenomena has been widely recognized in fields such as genetics and ecology. However, current methods are based upon rather ad-hoc deterministic algorithms. This project intends to develop formal statistical methods for carrying out boundary analysis, exploiting modern GIS tools to advance the development and interpretation of boundary analysis in spatial (cancer-related) maps. Attendant benefits of the project will include enhancements in the understanding of spatial structure associated with information displayed in cancer-related maps. Goals of this project include development of boundary analysis from an inferential perspective with evaluation of statistical modeling approaches using cancer data from the Minnesota Cancer Surveillance System (MCSS), the Iowa Women's Health Survey (IWHS), the Surveillance Epidemiology and End Results (SEER) (http://seer.cancer.gov) database of the National Cancer Institute, as well as Medicare usage and cancer hospice mortality data. Applications for environmental risk factor data from the Environmental Protection Agency (EPA) will also be carried out to draw toxin boundaries that may reveal interesting cancer-toxin relationships

说明（由申请人提供）：边界分析涉及空间地图中突变区的检测和分析。它在理解科学现象方面的重要性在遗传学和生态学等领域得到了广泛的认可。然而，目前的方法是基于相当ad-hoc确定性算法。该项目旨在制定进行边界分析的正式统计方法，利用现代地理信息系统工具推动空间（与癌症有关的）地图中边界分析的制定和解释。该项目的附带好处将包括增强对与癌症相关地图中显示的信息相关的空间结构的理解。该项目的目标包括从推理的角度开发边界分析，并使用来自明尼苏达州癌症监测系统（MCSS）、爱荷华州妇女健康调查（IWHS）、国家癌症研究所的监测流行病学和最终结果（SEER）（http：//www.example.com）数据库以及医疗保险使用和癌症临终关怀死亡率数据的癌症数据评估统计建模方法。seer.cancer.gov环境保护局（EPA）的环境风险因素数据的应用也将进行，以绘制可能揭示有趣的癌症-毒素关系的毒素边界