Accounting for Spatial Confounding in Epidemiological Studies with Individual-Level Exposures: An Exposure-Penalized Spline Approach
研究了空间模型在未测量空间混杂存在时可能增加偏倚的问题，提出一种暴露惩罚样条方法，通过选择空间平滑程度来减少偏倚，对个体水平暴露（如吸烟状态）效果有限，但对纯空间暴露（如建成环境）风险较高。
Abstract In the presence of unmeasured spatial confounding, spatial models may actually increase (rather than decrease) bias, leading to uncertainty as to how they should be applied in practice. We evaluated spatial modelling approaches through simulation and application to a big data electronic health record study. Whereas the risk of bias was high for purely spatial exposures (e.g. built environment), we found very limited potential for increased bias for individual-level exposures that cluster spatially (e.g. smoking status). We also proposed a novel exposure-penalized spline approach that selects the degree of spatial smoothing to explain spatial variability in the exposure. This approach appeared promising for efficiently reducing spatial confounding bias.