BACKGROUND Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questions about exposure mixtures, including methods such as weighted quantile sum (WQS) regression that estimate a joint effect of the mixture components. OBJECTIVES We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the inferential simplicity of WQS regression with the flexibility of g-computation, a method of causal effect estimation. We use simulations to examine whether quantile g-computation and WQS regression can accurately and precisely estimate the effects of mixtures in a variety of common scenarios. METHODS We examine the bias, confidence interval (CI) coverage, and bias-variance tradeoff of quantile g-computation and WQS regression and how these quantities are impacted by the presence of noncausal exposures, exposure correlation, unmeasured confounding, and nonlinearity of exposure effects. RESULTS Quantile g-computation, unlike WQS regression, allows inference on mixture effects that is unbiased with appropriate CI coverage at sample sizes typically encountered in epidemiologic studies and when the assumptions of WQS regression are not met. Further, WQS regression can magnify bias from unmeasured confounding that might occur if important components of the mixture are omitted from the analysis. DISCUSSION Unlike inferential approaches that examine the effects of individual exposures while holding other exposures constant, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding the effects of potential public health actions that act on exposure sources. Our approach may serve to help bridge gaps between epidemiologic analysis and interventions such as regulations on industrial emissions or mining processes, dietary changes, or consumer behavioral changes that act on multiple exposures simultaneously. https://doi.org/10.1289/EHP5838.

中文翻译：

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一种基于分位数的 g 计算方法来解决暴露混合物的影响。

背景暴露混合经常出现在许多领域的数据中，特别是在环境和营养流行病学领域。已经出现了各种策略来回答有关暴露混合物的问题，包括诸如加权分位数总和 (WQS) 回归之类的方法，这些方法估计混合物成分的联合效应。目标我们展示了一种估计混合物联合效应的新方法：分位数 g 计算。这种方法结合了 WQS 回归的推理简单性和 g 计算的灵活性，这是一种因果效应估计方法。我们使用模拟来检查分位数 g 计算和 WQS 回归是否可以准确而准确地估计混合物在各种常见场景中的影响。方法 我们检查偏差、置信区间 (CI) 覆盖率、分位数 g 计算和 WQS 回归的偏差-方差权衡，以及这些数量如何受到非因果暴露、暴露相关性、未测量混杂和暴露效应非线性的影响。结果 与 WQS 回归不同，分位数 g 计算允许在流行病学研究中通常遇到的样本量以及不满足 WQS 回归假设的情况下，通过适当的 CI 覆盖范围推断混合效应。此外，WQS 回归可以放大来自未测量混杂的偏差，如果在分析中省略了混合物的重要成分，则可能会出现这种偏差。讨论 与在保持其他暴露不变的情况下检查单个暴露的影响的推理方法不同，可以估计混合物影响的分位数 g 计算等方法对于了解对暴露源起作用的潜在公共卫生行动的影响至关重要。我们的方法可能有助于弥合流行病学分析和干预措施之间的差距，例如工业排放或采矿过程的规定、饮食变化或同时作用于多种暴露的消费者行为变化。https://doi.org/10.1289/EHP5838。