Keller Joshua P, Szpiro Adam A
Colorado State University, Fort Collins, CO, USA.
University of Washington, Seattle, WA, USA.
J R Stat Soc Ser A Stat Soc. 2020 Jun;183(3):1121-1143. doi: 10.1111/rssa.12556. Epub 2020 Mar 11.
Unmeasured, spatially-structured factors can confound associations between spatial environmental exposures and health outcomes. Adding flexible splines to a regression model is a simple approach for spatial confounding adjustment, but the spline degrees of freedom do not provide an easily interpretable spatial scale. We describe a method for quantifying the extent of spatial confounding adjustment in terms of the Euclidean distance at which variation is removed. We develop this approach for confounding adjustment with splines and using Fourier and wavelet filtering. We demonstrate differences in the spatial scales these bases can represent and provide a comparison of methods for selecting the amount of confounding adjustment. We find the best performance for selecting the amount of adjustment using an information criterion evaluated on an outcome model without exposure. We apply this method to spatial adjustment in an analysis of fine particulate matter and blood pressure in a cohort of United States women.
未测量的空间结构因素可能会混淆空间环境暴露与健康结果之间的关联。在回归模型中添加灵活样条是进行空间混杂调整的一种简单方法，但样条自由度并不能提供一个易于解释的空间尺度。我们描述了一种根据去除变异的欧几里得距离来量化空间混杂调整程度的方法。我们开发了这种使用样条以及傅里叶和小波滤波进行混杂调整的方法。我们展示了这些基所能代表的空间尺度的差异，并对选择混杂调整量的方法进行了比较。我们发现，使用在无暴露的结果模型上评估的信息准则来选择调整量时性能最佳。我们将此方法应用于对美国一组女性队列中细颗粒物与血压进行分析的空间调整。
