Lawson Andrew B
Arnold School of Public Health, University of South Carolina, USA.
Stat Med. 2006 Mar 15;25(5):897-916. doi: 10.1002/sim.2417.
This paper reviews issues in the analysis of non-focussed clustering, and proposes a novel approach to cluster modelling that can be used in a surveillance context. The novel approach involves the use of local likelihood models for the analysis of clustering in small area health data. Local likelihood is used when interdependence between data events at locations is modelled directly, as opposed to the modelling of a hidden process of cluster centres. This approach allows the use of conventional posterior sampling. It also allows a less parameterized approach to the form of clusters detected. The idea of a spatially dependent lasso which provides the local maxima for the aggregation of locations is considered as an approximation. The methods are applied to a well known data set and compared with Satscan, and a conditional logistic Bayesian model.
本文回顾了非聚焦聚类分析中的问题，并提出了一种可用于监测环境的聚类建模新方法。该新方法涉及使用局部似然模型来分析小区域健康数据中的聚类。当直接对位置处的数据事件之间的相互依赖性进行建模时，使用局部似然，这与对聚类中心的隐藏过程进行建模相反。这种方法允许使用传统的后验抽样。它还允许对检测到的聚类形式采用参数化程度较低的方法。考虑将提供位置聚集局部最大值的空间相关套索的概念作为一种近似。这些方法应用于一个知名数据集，并与Satscan和条件逻辑贝叶斯模型进行比较。
