Spectral adjustment for spatial confounding.
Spectral adjustment for spatial confounding.
复制标题
空间混杂的光谱调整。
DOI：
10.1093/biomet/asac069
复制
发表时间：
2023
期刊：
影响因子：
2.7
通讯作者：
Yang,Shu
中科院分区：
文献类型：
--
作者：
Guan,Yawen;Page,GarrittL;Reich,BrianJ;Ventrucci,Massimo;Yang,Shu
调整未测量的混杂因素通常是一个棘手的问题，但在空间环境中，在某些条件下是可能的。我们推导出暴露与不可测混杂因素之间的一致性的必要条件，以确保暴露的影响是可估计的。我们在谱域中指定我们的模型和假设，以允许在不同的空间分辨率下存在不同程度的混淆。确保可识别性的一个假设是，在全球尺度上存在的混淆在局部尺度上消散。我们表明，这种假设在谱域中相当于调整全球尺度的混淆在空间域中添加一个空间平滑版本的曝光的平均值的响应变量。在这个总体框架内，我们提出了一系列的混淆调整方法，范围从参数调整的基础上Matérn相干函数更强大的半参数方法，使用平滑样条。这些想法适用于模拟和真实的数据集的面积和地质统计数据。
Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions. One assumption that ensures identifiability is that confounding present at global scales dissipates at local scales. We show that this assumption in the spectral domain is equivalent to adjusting for global-scale confounding in the spatial domain by adding a spatially smoothed version of the exposure to the mean of the response variable. Within this general framework, we propose a sequence of confounder adjustment methods that range from parametric adjustments based on the Matérn coherence function to more robust semiparametric methods that use smoothing splines. These ideas are applied to areal and geostatistical data for both simulated and real datasets.