Statistical Methods in Cancer Control and Epidemiology

描述（由申请人提供）：被称为地理信息系统（gis）的复杂计算机程序通过在公共研究区域上“分层”多个数据源的能力，彻底改变了空间参考数据集的分析。然而，对这些复杂且经常在空间和时间上不一致的数据集进行统计推断的方法现在才刚刚开始发展。在本建议中，我们在与癌症控制和流行病学有关的七个具体目标领域开发空间统计方法。首先，我们考虑了癌症控制的层次模型，开发了单变量和多变量模型来分析癌症死亡率、发病率、分期和筛查数据。其次，我们提出了共同的空间因子模型来解释不同地区癌症死亡率或发病率之间的相关性。第三，我们开发了增强的空间晶格模型，用于探索各种社区因素（如吸烟水平、教育、贫困、医疗保健获取等）与癌症相关行为（如乳房检查频率）之间的关系。第四，我们提出了灵活的空间过程模型，利用共区域化来建模多变量致癌物数据。第五，我们将空间CDF的概念推广到协变量加权、条件和全双变量版本，并提出将其用于分析可能的多变量癌症相关暴露。第六，建立空间治愈率模型，并将其应用于空间相关的戒烟率数据。第七，我们提出了空间定向梯度方法，可以识别和推断致癌物质表面的空间变化率。我们提供了几个与癌症相关的例子来说明我们提出的方法，以及我们将我们的方法所需的马尔可夫链蒙特卡罗计算与现有GIS制图和数据库工具联系起来的愿景的概述。

DESCRIPTION (provided by applicant): Sophisticated computer programs known as geographic information systems (GISs) have revolutionized the analysis of spatially referenced datasets, through their ability to "layer" multiple data sources over a common study area. However, methods for statistical inference on these complex and often spatially and temporally misaligned datasets are only now beginning to develop. In this proposal we develop spatial statistical methodology in seven specific aim areas related to cancer control and epidemiology. First, we consider hierarchical models for cancer control, developing both univariate and multivariate models for analyzing cancer mortality, incidence, staging, and screening data. Second, we propose common spatial factor models for explaining correlations among cancer mortality or incidence rates at different locations. Third, we develop enhanced spatial lattice models for exploring the relationship between various community factors (e.g. smoking levels, education, poverty, health care access, etc.) and cancer-related behaviors (e.g. frequency of breast exam). Fourth, we propose flexible spatial process models for modeling multivariate carcinogen data, using coregionalization. Fifth, we generalize the notion of the spatial CDF to covariate-weighted, conditional, and fully bivariate versions, and propose its use in analyzing possibly multivariate cancer-related exposures. Sixth, we develop spatial cure rate models, and suggest their application to spatially associated smoking quit rate data. Seventh, we propose spatial directional gradient methods that enable identification of and inference for spatial rates of change in carcinogen surfaces. We provide several cancer-related examples to illustrate the methods we propose, as well as an outline of our vision for linking the Markov chain Monte Carlo computing our methods require with existing GIS mapping and database tools.