空间流行病学研究中的贝叶斯统计方法  

Bayesian statistics in spatial epidemiology

作　　者：郑卫军[1] 李秀央[1] 陈坤[1] 

机构地区：[1]浙江大学医学院流行病与卫生统计系,浙江杭州310058

出　　处：《浙江大学学报（医学版）》2008年第6期642-647,共6页Journal of Zhejiang University(Medical Sciences)

基　　金：杭州市科技局科技创新重点资助项目(G20050651)

摘　　要：贝叶斯统计学通过构建分层贝叶斯模型,结合马尔科夫链-蒙特卡罗方法,可以有效地对包括小范围区域疾病分布图的描绘、疾病地理聚集性分析研究和疾病地理相关性研究在内的时空非独立数据进行分析。目前贝叶斯空间分析模型已发展成多类的分析方法,包括近年来充分发展和得到应用的BYM模型、联合随机模型、半参数贝叶斯统计及移动性均化模型等。同时,通过离差信息准则,开展了众多的模型比较分析;其中,以BYM模型和半参数模型中的MIX模型的优势较明显。随着进一步的深入研究,贝叶斯统计学在空间流行病学研究中发挥的空间将进一步增大。Through the multi-stage hierarchical Bayesian model and Markov Chain Monte Carlo methods ,Bayesian statistics can be used in dependent spatial data analysis ,including disease mapping in small areas ,disease clustering ,and geographical correlation studies. Recently ,Bayesian spatial models have been developed with many types, which have made considerable progress in data analysis. This paper introduces several approaches that have been fully developed and applied, such as BYM model, joint model, semi-parameter model, moving average model and so on. Recently, many studies focused on the comparison work through Deviance Information criterion. Those results show that BYM model and MIX model of semi-parameter model could obtain better results. As more research going on, Bayesian statistics will have more space in applications of spatial epidemiology.

关 键 词：贝叶斯定理 统计学 贝叶斯统计 空间流行病学 空间分析 

分 类 号：R181.34[医药卫生—流行病学]