Everything all at once: On choosing an estimand for multi-component environmental exposures
多成分环境暴露的目标量选择：联合位移如何定义AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确讨论多成分暴露的因果目标量及单期、纵向位移干预的非参数估计。
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多成分环境暴露的目标量选择：联合位移如何定义
- 研究问题
- 多种连续环境暴露共同作用时，如何定义单期或纵向暴露组合位移的目标量，并在研究不同成分及其交互作用时减少数据外推？
- 方法与思路
- 作者允许灵活设定一个或多个暴露成分的位移，评估并修改超出数据支持的位移，再使用机器学习进行完全非参数的关系估计。
- 材料陈述的贡献
- 摘要称，所讨论目标量具有类似主效应回归系数的解释，并提供外推评估、位移调整和逐步代码，以纵向农药暴露与高血压风险展示应用。
- 阅读关注点
- 应核对位移目标量的精确定义、数据支持和外推诊断、纵向因果识别所需条件，以及机器学习拟合如何进入估计与不确定性计算。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验。摘要未完整说明识别和推断条件，也未报告农药暴露与高血压关系的数值或方向。
原文摘要
Many research questions -- particularly those in environmental health -- do not involve binary exposures. In environmental epidemiology, this includes multivariate exposure mixtures with nondiscrete components. Causal inference estimands and estimators to quantify the relationship between an exposure mixture and an outcome are relatively few. We propose an approach to quantify a relationship between a shift in the exposure mixture and the outcome -- either in the single timepoint or longitudinal setting. The shift in the exposure mixture can be defined flexibly in terms of shifting one or more components, including examining interaction between mixture components, and in terms of shifting the same or different amounts across components. The estimand we discuss has a similar interpretation as a main effect regression coefficient. First, we focus on choosing a shift in the exposure mixture supported by observed data. We demonstrate how to assess extrapolation and modify the shift to minimize reliance on extrapolation. Second, we propose estimating the relationship between the exposure mixture shift and outcome completely nonparametrically, using machine learning in model-fitting. This is in contrast to other current approaches, which employ parametric modeling for at least some relationships, which we would like to avoid because parametric modeling assumptions in complex, nonrandomized settings are tenuous at best. We are motivated by longitudinal data on pesticide exposures among participants in the CHAMACOS Maternal Cognition cohort. We examine the relationship between longitudinal exposure to agricultural pesticides and risk of hypertension. We provide step-by-step code to facilitate the easy replication and adaptation of the approaches we use.