Bayesian Multivariate Areal Wombling for Multiple Disease Boundary Analysis
Bayesian Multivariate Areal Wombling for Multiple Disease Boundary Analysis
复制标题
DOI：
10.1214/07-ba211
复制
发表时间：
2007-01-01
影响因子：
4.4
通讯作者：
Carlin, Bradley P.
中科院分区：
文献类型：
--
作者：
Ma, Haijun;Carlin, Bradley P.
按地区单位（县、邮政编码等）汇总的多变量数据在公共卫生领域很常见。估计或测试这些数据的地理边界可能有不同的目标。例如，对于多种疾病结果的数据，我们可能对所有疾病的一组“复合”边界感兴趣，或者对每种疾病的单独边界感兴趣，或者对两者都感兴趣。为了满足这些不同的要求，需要采用不同的区域模糊（边界分析）技术。但无论如何，潜在的统计模型需要考虑疾病和地点之间的相关性。利用多元条件自回归（MCAR）分布和空间结构方程建模的最新发展，我们提出了多种贝叶斯分层模型用于多元面边界分析，包括一些包含随机邻域结构的模型。我们的许多模型都可以通过标准软件实现，即用于后验抽样的WinBUGS和用于总结和绘图的R。我们使用明尼苏达州县级食道癌、喉癌和肺癌数据来说明我们的方法，比较了两种、一种或两种相关性的模型。我们确定了复合边界和癌症特异性边界，使用DIC标准选择最佳统计模型。我们的研究结果表明，将以采矿和旅游为导向的东北县与该州其他地区分开的复合响应面和癌症特异性响应面的主要边界，以及双城都市区的次要（残余）边界。
Multivariate data summarized over areal units (counties, zip codes, etc.) are common in the field of public health. Estimation or testing of geographic boundaries for such data may have varied goals. For example, for data on multiple disease outcomes, we may be interested in a single set of "composite" boundaries for all diseases, separate boundaries for each disease, or both. Different areal wombling (boundary analysis) techniques are needed to meet these different requirements. But in any case, the underlying statistical model needs to account for correlations across both diseases and locations. Utilizing recent developments in multivariate conditionally autoregressive (MCAR) distributions and spatial structural equation modeling, we suggest a variety of Bayesian hierarchical models for multivariate areal boundary analysis, including some that incorporate random neighborhood structure. Many of our models can be implemented via standard software, namely WinBUGS for posterior sampling and R for summarization and plotting. We illustrate our methods using Minnesota county level esophagus, larynx, and lung cancer data, comparing models that account for both, only one, or neither of the aforementioned correlations. We identify both composite and cancer-specific boundaries, selecting the best statistical model using the DIC criterion. Our results indicate primary boundaries in both the composite and cancer-specific response surface separating the mining-and tourism-oriented north east counties from the remainder of the state, as well as secondary (residual) boundaries in the Twin Cities metro area.