Adjustment for Unmeasured Spatial Confounding in Settings of Continuous Exposure Conditional on the Binary Exposure Status: Conditional Generalized Propensity Score-Based Spatial Matching
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二元暴露状态下连续暴露的条件广义倾向评分空间匹配
- 研究问题
- 当连续暴露依赖二元暴露状态，且两类暴露属性都存在空间混杂时，如何通过匹配调整未测量空间混杂以研究暴露效应？
- 方法与思路
- 作者分别估计二元状态的倾向评分与给定状态的条件广义倾向评分，再结合空间信息和地理邻近性匹配暴露与未暴露单位。
- 材料陈述的贡献
- 摘要称该方法便于检查协变量平衡，模拟支持其调整空间混杂的能力；应用报告炼油生产与卒中患病率正相关，并提供 R 包。
- 阅读关注点
- 应核对连续暴露效应的具体目标、未测量混杂可被空间匹配调整的条件，以及共同支持、平衡诊断与空间尺度选择。
- 阅读边界
- 这是 arXiv 预印本，同行评审与接收未核实；摘要未说明完整识别假设，应用中的正相关不能仅凭摘要解释为因果效应。
原文摘要
Propensity score (PS) matching to estimate causal effects of exposure is biased when unmeasured spatial confounding exists. Some exposures are continuous yet dependent on a binary variable (e.g., level of a contaminant (continuous) within a specified radius from residence (binary)). Further, unmeasured spatial confounding may vary by spatial patterns for both continuous and binary attributes of exposure. We propose a new generalized propensity score (GPS) matching method for such settings, referred to as conditional GPS (CGPS)-based spatial matching (CGPSsm). A motivating example is to investigate the association between proximity to refineries with high petroleum production and refining (PPR) and stroke prevalence in the southeastern United States. CGPSsm matches exposed observational units (e.g., exposed participants) to unexposed units by their spatial proximity and GPS integrated with spatial information. GPS is estimated by separately estimating PS for the binary status (exposed vs. unexposed) and CGPS on the binary status. CGPSsm maintains the salient benefits of PS matching and spatial analysis: straightforward assessments of covariate balance and adjustment for unmeasured spatial confounding. Simulations showed that CGPSsm can adjust for unmeasured spatial confounding. Using our example, we found positive association between PPR and stroke prevalence. Our R package, CGPSspatialmatch, has been made publicly available.