Choi Jungsoon, Lawson Andrew B, Cai Bo, Hossain Md Monir
Division of Biostatistics and Epidemiology, College of Medicine, Medical University of South Carolina.
Environmetrics. 2011 Dec;22(8):1008-1022. doi: 10.1002/env.1127.
Health outcomes are linked to air pollution, demographic, or socioeconomic factors which vary across space and time. Thus, it is often found that relative risks in space-time health data have locally different temporal patterns. In such cases, latent modeling is useful in the disaggregation of risk profiles. In particular, spatio-temporal mixture models can help to isolate spatial clusters each of which has a homogeneous temporal pattern in relative risks. In mixture modeling, various weight structures can be used and two situations can be considered: the number of underlying components is known or unknown. In this paper, we compare spatio-temporal mixture models with different weight structures in both situations. In addition, spatio-temporal Dirichlet process mixture models are compared to them when the number of components is unknown. For comparison, we propose a set of spatial cluster detection diagnostics based on the posterior distribution of the weights. We also develop new accuracy measures to assess the recovery of true relative risks. Based on the simulation study, we examine the performance of various spatio-temporal mixture models in terms of proposed methods and goodness-of-fit measures. We apply our models to a county-level chronic obstructive pulmonary disease data set from the state of Georgia.
健康结果与空气污染、人口统计学或社会经济因素相关，这些因素会随时间和空间而变化。因此，人们常常发现时空健康数据中的相对风险具有局部不同的时间模式。在这种情况下，潜在模型对于风险概况的分解很有用。特别是，时空混合模型有助于分离出空间集群，每个集群在相对风险方面都有同质的时间模式。在混合建模中，可以使用各种权重结构，并且可以考虑两种情况：潜在成分的数量已知或未知。在本文中，我们比较了两种情况下具有不同权重结构的时空混合模型。此外，当成分数量未知时，将时空狄利克雷过程混合模型与它们进行比较。为了进行比较，我们基于权重的后验分布提出了一组空间集群检测诊断方法。我们还开发了新的准确性度量来评估真实相对风险的恢复情况。基于模拟研究，我们根据提出的方法和拟合优度度量来检验各种时空混合模型的性能。我们将我们的模型应用于来自佐治亚州的县级慢性阻塞性肺疾病数据集。
