Statistical Methods in Cancer Control and Epidemiology

描述(由申请人提供)：项目摘要被称为地理信息系统(GISS)的复杂计算机程序通过其在共同研究区域内对多个数据源进行“分层”的能力，彻底改变了对空间参考数据集的分析。然而，对这些复杂数据集进行统计推断的方法现在才刚刚开始发展。在这项建议中，我们开发了与癌症控制和流行病学相关的五个特定目标领域的统计方法。首先，我们开发了分层预测过程方法，以缓解在将地质统计模型拟合到大数据集时需要重复求逆大矩阵的相关问题。第二，我们提出了处理多变量标记点过程的新方法，就像空间点模式所需要的那样，其中点是通过癌症的类型或每个人的治疗选择来标记点的。第三，我们考虑了使用混合Polya树的癌症生存数据的半参数分层模型。第四，我们考虑对来自纵向实验的连续时间时空数据的分析，这些实验旨在估计函数关系。第五，描述一套R包，它们帮助将必要的地理参考数据库和显示组件与分层建模能力相结合，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：这项工作与公共健康的相关性在于它能够提高州政府专业人员的理解和决策能力，这些专业人员从事综合癌症控制计划的规划。我们的重点是癌症研究中的实际问题，包括确定居住在距离放射治疗设施较远的乳腺癌患者是否显著更有可能选择乳房切除术而不是保乳术，并调查不同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更高的皮肤癌发病率有关。

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.