Yan Ping, Clayton Murray K
Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, USA.
Stat Med. 2006 Mar 15;25(5):867-81. doi: 10.1002/sim.2424.
Modelling disease clustering over space and time can be helpful in providing indications of possible exposures and planning corresponding public health practices. Though a considerable number of studies focus on modelling spatio-temporal patterns of disease, most of them do not directly model a spatio-temporal clustering structure and could be ineffective for detecting clusters. In this paper, we extend a purely spatial cluster model to accommodate space-time clustering. Inference is performed in a Bayesian framework using reversible jump Markov chain Monte Carlo. This idea is illustrated using data on female breast cancer mortality from Japan. A hierarchical parametric space-time model for mapping disease is used for comparison.
对疾病在空间和时间上的聚集情况进行建模，有助于指明可能的暴露因素，并规划相应的公共卫生措施。尽管大量研究聚焦于对疾病的时空模式进行建模，但其中大多数并未直接对时空聚集结构进行建模，可能无法有效检测聚集情况。在本文中，我们扩展了一个纯空间聚类模型，以适应时空聚类。在贝叶斯框架下，使用可逆跳跃马尔可夫链蒙特卡罗方法进行推断。利用日本女性乳腺癌死亡率数据对这一想法进行了说明。使用一个用于绘制疾病分布的分层参数时空模型进行比较。
