Am J Epidemiol. 2021 Dec 1;190(12):2658-2661. doi: 10.1093/aje/kwab142.
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.
伴随文章由 Keil 等人撰写（Am J Epidemiol. 2021;190(12):2647-2657），运用贝叶斯 g-计算来探究 6 种与发电站排放有关的空气金属暴露对出生体重的因果效应。这样做阐明了将环境混合物分析作为一个明确对比的潜在价值，该对比可能是针对一个明确干预措施的反应——此处是燃煤电厂的退役。将混合物分析框架设定为近似的“目标试验”是一种重要的方法，值得纳入关于环境混合物分析的已有丰富文献中。然而，它在发电厂例子中的应用凸显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，在环境混合物的研究中，这种不一致似乎特别困难。贝叶斯方法，如模型平均和信息先验，可以提供帮助，但最终对于克服这一突出挑战是有限的。
