Bayesian detection of clusters and discontinuities in disease maps
Bayesian detection of clusters and discontinuities in disease maps
复制标题
DOI：
10.1111/j.0006-341x.2000.00013.x
复制
发表时间：
2000-03-01
期刊：
影响因子：
1.9
通讯作者：
Rasser, G
中科院分区：
文献类型：
--
作者：
Knorr-Held, L;Rasser, G
一个有趣的流行病学问题是分析疾病发病率或死亡率的地理差异。这种分析的一个目标是检测风险升高(或降低)的集群，以便确定与疾病有关的未知风险因素。我们基于Green(1995，Biillska 82,711-732)的可逆跳跃MCMC方法，提出了一种非参数贝叶斯方法来检测这类簇。以前的模型假设地理区域可以被组合成集群，集群内的相对风险恒定。集群的数量、集群的位置以及每个集群内的风险都是未知的。这种规范可以看作是不规则、离散空间中的变维变点问题。我们通过对德国口腔癌死亡率的分析来说明我们的方法，并将结果与常用的贝萨格、约克和莫利(1991年，统计数学研究所年鉴，43，1-59)的贝叶斯疾病图谱方法得到的结果进行比较。
An interesting epidemiological problem is the analysis of geographical variation in rates of disease incidence or mortality. One goal of such an analysis is to detect clusters of elevated (or lowered) risk in order to identify unknown risk factors regarding the disease. We propose a nonparametric Bayesian approach for the detection of such clusters based on Green's (1995, Biometrika 82, 711-732) reversible jump MCMC methodology. The prior model assumes that geographical regions can be combined in clusters with constant relative risk within a cluster. The number of clusters, the location of the clusters, and the risk within each cluster is unknown. This specification can be seen as a change-point problem of variable dimension in irregular, discrete space. We illustrate our method through an analysis of oral cavity cancer mortality rates in Germany and compare the results with those obtained by the commonly used Bayesian disease mapping method of Besag, York, and Mollie (1991, Annals of the Institute of Statistical Mathematics, 43, 1-59).