Louie Mary M, Kolaczyk Eric D
Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA.
Stat Med. 2006 Apr 30;25(8):1287-306. doi: 10.1002/sim.2276.
The effects of spatial scale in disease mapping are well-recognized, in that the information conveyed by such maps varies with scale. Here we provide an inferential framework, in the context of tract count data, for describing the distribution of relative risk simultaneously across a hierarchy of multiple scales. In particular, we offer a multiscale extension of the canonical standardized mortality ratio (SMR), consisting of Bayesian posterior-based strategies for both estimation and characterization of uncertainty. As a result, a hierarchy of informative disease and confidence maps can be produced, without the need to first try to identify a single appropriate scale of analysis. We explore the behaviour of the proposed methodology in a small simulation study, and we illustrate its usage through an application to data on gastric cancer in Tuscany.
疾病地图中空间尺度的影响已得到充分认识，因为此类地图所传达的信息会随尺度而变化。在此，我们在区域计数数据的背景下提供一个推理框架，用于描述相对风险在多个尺度层次上的同时分布。特别是，我们提供了经典标准化死亡率（SMR）的多尺度扩展，包括基于贝叶斯后验的估计策略和不确定性表征策略。因此，可以生成一系列信息丰富的疾病地图和置信度地图，而无需首先尝试确定单一的合适分析尺度。我们在一个小型模拟研究中探索了所提出方法的行为，并通过将其应用于托斯卡纳地区的胃癌数据来说明其用法。
