A geostatistical approach to large-scale disease mapping with temporal misalignment.
A geostatistical approach to large-scale disease mapping with temporal misalignment.
复制标题
具有时间错位的大规模疾病绘图的地统计方法。
DOI：
10.1111/j.1541-0420.2011.01721.x
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发表时间：
2012
期刊：
影响因子：
1.9
通讯作者：
Coull,BrentA
中科院分区：
文献类型：
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作者：
Hund,Lauren;Chen,JarvisT;Krieger,Nancy;Coull,BrentA
当区域边界跨时间移位时（例如，人口普查区域边界在每个人口普查年度都会发生变化），使跨空间的时间趋势建模复杂化。在实践中，具有时间边界不对齐的大型区域级数据集变得越来越普遍。时间错位数据的几个现有的方法不占空间随机效应随时间的相关性。为了克服与时间错位相关的问题，我们构建了一个地质统计模型的总计数数据，假设一个潜在的连续的风险表面诱导区域之间的空间相关性。我们实现了一个广义线性混合模型的框架内使用径向基样条的模型。使用这种方法，边界不对准就不再是问题了。此外，这种疾病映射框架通过使用惩罚准似然近似最大似然估计来促进快速，简单的模型拟合。我们预计，该方法也将是有用的大型疾病映射数据集，完全贝叶斯方法是不可行的。我们应用我们的方法来评估1988-1992年和1998-2002年期间洛杉矶乳腺癌发病率的社会经济趋势。
Temporal boundary misalignment occurs when area boundaries shift across time (e.g., census tract boundaries change at each census year), complicating the modeling of temporal trends across space. Large area-level datasets with temporal boundary misalignment are becoming increasingly common in practice. The few existing approaches for temporally misaligned data do not account for correlation in spatial random effects over time. To overcome issues associated with temporal misalignment, we construct a geostatistical model for aggregate count data by assuming that an underlying continuous risk surface induces spatial correlation between areas. We implement the model within the framework of a generalized linear mixed model using radial basis splines. Using this approach, boundary misalignment becomes a nonissue. Additionally, this disease-mapping framework facilitates fast, easy model fitting by using a penalized quasilikelihood approximation to maximum likelihood estimation. We anticipate that the method will also be useful for large disease-mapping datasets for which fully Bayesian approaches are infeasible. We apply our method to assess socioeconomic trends in breast cancer incidence in Los Angeles between the periods 1988–1992 and 1998–2002.