Tzala Evangelia, Best Nicky
Hellenic Centre for Diseases Control and Prevention, Athens, Greece.
Stat Methods Med Res. 2008 Feb;17(1):97-118. doi: 10.1177/0962280207081243. Epub 2007 Sep 13.
In this article, three alternative Bayesian hierarchical latent factor models are described for spatially and temporally correlated multivariate health data. The fundamentals of factor analysis with ideas of space- time disease mapping to provide a flexible framework for the joint analysis of multiple-related diseases in space and time with a view to estimating common and disease-specific trends in cancer risk are combined. The models are applied to area-level mortality data on six diet-related cancers for Greece over the 20-year period from 1980 to 1999. The aim of this study is to uncover the spatial and temporal patterns of any latent factor(s) underlying the cancer data that could be interpreted as reflecting some aspects of the habitual diet of the Greek population.
本文描述了三种用于空间和时间相关的多变量健康数据的贝叶斯分层潜在因素模型。结合了因子分析的基本原理与时空疾病映射的概念，以提供一个灵活的框架，用于在空间和时间上联合分析多种相关疾病，旨在估计癌症风险中的共同趋势和特定疾病趋势。这些模型应用于1980年至1999年这20年间希腊六种与饮食相关癌症的地区层面死亡率数据。本研究的目的是揭示癌症数据背后任何潜在因素的时空模式，这些模式可被解释为反映了希腊人群习惯饮食的某些方面。
