Disease cluster detection: A critique and a Bayesian proposal
Disease cluster detection: A critique and a Bayesian proposal
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DOI：
10.1002/sim.2417
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发表时间：
2006-03-15
影响因子：
2
通讯作者：
Lawson, AB
中科院分区：
文献类型：
--
作者：
Lawson, AB
本文回顾了非聚焦聚类分析中的问题，并提出了一种新的方法，集群建模，可用于在监督的情况下。新的方法涉及使用局部似然模型的聚类分析在小区域的健康数据。当地的可能性是使用时，数据事件之间的相互依存关系的位置是直接建模，而不是一个隐藏的过程的集群中心建模。这种方法允许使用传统的后验采样。它还允许对检测到的簇的形式采用较少参数化的方法。一个空间依赖的套索，提供了当地的最大值的位置聚合的想法被认为是一个近似。该方法被应用到一个众所周知的数据集，并与Satscan，和条件Logistic贝叶斯模型进行比较。版权所有（c）2006约翰威利父子有限公司。
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. Copyright (c) 2006 John Wiley & Sons, Ltd.