Joint Modeling of Multiple Spatial Temporal Outcomes

多个时空结果的联合建模

• 批准号: 436102-2013
• 负责人: Feng, CindyXin
• 金额: $ 1.09万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=574275
• 关键词: Joint; Modeling; Multiple; Spatial; Temporal

In public health, environmental and ecological studies, variables measured at the same spatial locations may be correlated so that the spatial structures of such variables across the region under consideration are very similar, indicating that they may be characterized by a common spatial risk surface. Employing such a commonality in risks may be useful for gaining precision of local area risk estimates, especially for rare diseases. Methods for clustering functional curves over space are also in high demand in many areas of scientific research as a useful screening tool for further epidemiological investigations. My proposed research program aims at furthering methods in those two related topics: joint modeling of multiple spatial outcomes and clustering of spatially correlated functional data. An integrated approach blending the modeling techniques from those two areas will be developed for testing common clustering pattern across multiple outcomes. Such an approach will be very helpful for identifying common latent risk factors, such as variations in social structure and policy implementation driving the common clustering patterns. The common spatial factor model will be extended to model spatial-temporal correlated multiple zero-heavy outcomes, such as counts of low birth weight and premature birth. In addition, a joint modeling framework of spatially correlated longitudinal and survival outcomes will be developed, which will be applied to jointly modeling the duration of wildfire and occurrence of fire ignition for British Columbia forestry fire data. To advance the methods for clustering functional curves over space, Bayesian hierarchical models will be developed and compared with conventional methods, in terms of misclassification of the cluster membership and their impact on covariate effects. The modeling strategy will also be applied to clustering hazard functions for time to event data. Methods accounting for discontinuities in the risk surface will also be developed to identify clustering of regions with substantially different temporal patterns compared with surrounding regions.

在公共卫生、环境和生态研究中，在相同空间位置测量的变量可能是相关的，因此所考虑区域内此类变量的空间结构非常相似，表明它们可能具有共同的空间风险面。利用风险的这种共性可能有助于获得局部区域风险估计的准确性，特别是对于罕见疾病。在许多科学研究领域，对空间函数曲线聚类的方法也有很高的需求，作为进一步流行病学调查的有用筛选工具。 我提出的研究计划旨在进一步推进这两个相关主题的方法：多个空间结果的联合建模和空间相关功能数据的聚类。将开发一种融合这两个领域的建模技术的集成方法，用于测试跨多个结果的常见聚类模式。这种方法对于识别常见的潜在风险因素非常有帮助，例如驱动常见集群模式的社会结构和政策实施的变化。通用空间因素模型将扩展到模拟时空相关的多个零重结果，例如低出生体重和早产的计数。此外，还将开发一个空间相关的纵向和生存结果的联合建模框架，该框架将应用于对不列颠哥伦比亚省林业火灾数据的野火持续时间和火灾发生进行联合建模。为了推进空间上聚类功能曲线的方法，将开发贝叶斯分层模型，并在聚类成员的错误分类及其对协变量效应的影响方面与传统方法进行比较。该建模策略还将应用于对事件时间数据的危险函数进行聚类。还将开发考虑风险面不连续性的方法，以识别与周围区域相比具有显着不同时间模式的区域的聚类。