Statistical Methods in Cancer Control and Epidemiology

被称为地理信息系统（GIS）的复杂的计算机程序已经彻底改变了空间参考数据集的分析，通过它们在一个共同的研究领域“分层”多个数据源的能力。然而，对这些复杂数据集进行统计推断的方法现在才开始发展。在本提案中，我们在与癌症控制和流行病学相关的五个特定目标领域开发了统计方法。首先，我们开发了分层预测过程方法，以缓解与需要反复反转大矩阵拟合地质统计模型到大数据集相关的问题。第二，我们提出了新的方法来处理多变量标记点过程，将需要一个空间点模式，其中的点标记的类型的癌症或治疗选择的每一个人。第三，我们考虑半参数层次模型的癌症生存数据使用混合的Polya树。第四，我们考虑分析连续时间时空数据所产生的纵向实验，旨在估计功能的关系。第五，描述一套R软件包，它有助于将必要的地理参考数据库和显示组件与分层建模功能集成在一起，从而将我们提出的分层空间分析带给比目前可能的更广泛的潜在受众。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。公共卫生相关性：这项工作与公共卫生的相关性在于它能够提高参与规划全面癌症控制计划的州专业人员的理解和决策能力。我们的重点是正视癌症研究中的真实的问题，包括确定居住在离放射治疗设施较远的乳腺癌妇女是否更有可能选择乳房切除术而不是乳房保留手术（"乳房肿瘤切除术”），并调查地理区域估计的紫外线暴露水平随时间的变化，以及这些水平是否与皮肤癌的高发病率有关。

DESCRIPTION (provided by applicant): Project Summary 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 datasets are only now beginning to develop. In this proposal we develop statistical methodology in five specific aim areas related to cancer control and epidemiology. First, we develop hierarchical predictive process approaches to easing the problems associated with the need to repeatedly invert large matrices in fitting geostatistical models to large datasets. Second, we propose new methods for handling multivariate marked point processes, as would be required for a spatial point pattern where the points are marked by the type of cancer or perhaps treatment selection of each individual. Third, we consider semiparametric hierarchical models for cancer survival data using mixtures of Polya trees. Fourth, we consider the analysis of continuous-time spatiotemporal data arising from longitudinal experiments designed to estimate functional relationships. Fifth, describe a suite of R packages that help integrate necessary georeferenced database and display components with hierarchical modeling capability, thus bringing the hierarchical spatial analysis we propose to a far broader potential audience than is currently possible. We provide several cancer-related examples to illustrate the methods we propose. PUBLIC HEALTH RELEVANCE: The relevance of this work to public health lies in its ability to improve the understanding and decision-making abilities of state-based professionals engaged in planning for comprehensive cancer control programs. Our focus is squarely on real problems in cancer research, including determining whether women with breast cancer who live further from radiation treatment facilities are significantly more likely to opt for mastectomy over breast conserving surgery (``lumpectomy"), and investigating the change in estimated UV exposure levels over time by geographic region, and whether these levels are associated with higher rates of skin cancer.