Invited Commentary: The Promise and Pitfalls of Causal Inference With Multivariate Environmental Exposures.
Invited Commentary: The Promise and Pitfalls of Causal Inference With Multivariate Environmental Exposures.
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特邀评论：多元环境暴露因果推理的前景和陷阱。
DOI：
10.1093/aje/kwab142
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发表时间：
2021
影响因子：
5
通讯作者：
Zigler,CorwinM
中科院分区：
文献类型：
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作者：
Zigler,CorwinM
Keil等人（Am J Epidemiol.二零二一年; 190（12）：2647-2657）部署贝叶斯g计算来调查与发电厂排放有关的6种空气中金属暴露对出生体重的因果影响。在这样做的时候，它阐明了潜在的价值框架分析的环境混合物之间的暴露分布，可能会出现在一个明确的干预，在这里，退役的煤电厂之间的明确对比。框架的混合物分析作为一个近似的“目标试验”是一个重要的方法，值得纳入已经丰富的文献分析环境混合物。然而，在发电厂的例子中，它的部署突出了当目标试验与数据中观察到的暴露分布不一致时可能出现的挑战，这种不一致在环境混合物的研究中似乎特别困难。贝叶斯方法，如模型平均和信息先验可以帮助，但他们最终是有限的克服这一突出的挑战。
The accompanying article by Keil et al. (Am J Epidemiol. 2021;190(12):2647–2657) deploys Bayesian g-computation to investigate the causal effect of 6 airborne metal exposures linked to power-plant emissions on birth weight. In so doing, it articulates the potential value of framing the analysis of environmental mixtures as an explicit contrast between exposure distributions that might arise in response to a well-defined intervention—here, the decommissioning of coal plants. Framing the mixture analysis as that of an approximate “target trial” is an important approach that deserves incorporation into the already rich literature on the analysis of environmental mixtures. However, its deployment in the power plant example highlights challenges that can arise when the target trial is at odds with the exposure distribution observed in the data, a discordance that seems particularly difficult in studies of environmental mixtures. Bayesian methodology such as model averaging and informative priors can help, but they are ultimately limited for overcoming this salient challenge.