Departments of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, USA.
Stat Med. 2010 Sep 30;29(22):2325-37. doi: 10.1002/sim.3984.
Maps of estimated disease rates over multiple time periods are useful tools for gaining etiologic insights regarding potential exposures associated with specific locations and times. In this paper, we describe an extension of the Gangnon-Clayton model for spatial clustering to spatio-temporal data. As in the purely spatial model, a large set of circular regions of varying radii centered at observed locations are considered as potential clusters, e.g. subregions with a different pattern of risk than the remainder of the study region. Within the spatio-temporal model, no specific parametric form is imposed on the temporal pattern of risk within each cluster. In addition to the clusters, the proposed model incorporates spatial and spatio-temporal heterogeneity effects and can readily accommodate regional covariates. Inference is performed in a Bayesian framework using MCMC. Although formal inferences about the number of clusters could be obtained using a reversible jump MCMC algorithm, we use local Bayes factors from models with a fixed, but overly large, number of clusters to draw inferences about both the number and the locations of the clusters. We illustrate the approach with two applications of the model to data on female breast cancer mortality in Japan and evaluate its operating characteristics in a simulation study.
多时间段疾病估计率图是一种有用的工具，可以深入了解与特定地点和时间相关的潜在暴露因素的病因。本文描述了对空间聚类模型的扩展，以适应时空数据。与纯粹的空间模型一样，研究人员考虑了一组以观察位置为中心、半径不同的圆形区域作为潜在的聚类，例如与研究区域其他部分风险模式不同的子区域。在时空模型中，没有对每个聚类内的风险的时间模式施加特定的参数形式。除了聚类，所提出的模型还包含空间和时空异质性效应，并可以方便地包含区域协变量。推理是在贝叶斯框架中使用 MCMC 进行的。虽然可以使用可逆跳跃 MCMC 算法对聚类的数量进行正式推断，但我们使用来自具有固定但过大数量聚类的模型的局部贝叶斯因子来推断聚类的数量和位置。我们通过将模型应用于日本女性乳腺癌死亡率数据的两个实例来说明该方法，并在模拟研究中评估其运行特性。
