Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA.
Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA.
Environ Health Perspect. 2020 Apr;128(4):47004. doi: 10.1289/EHP5838. Epub 2020 Apr 7.
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.
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.
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.
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.
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.
在许多领域的数据中，经常会出现暴露混合物，尤其是在环境和营养流行病学领域。为了回答关于暴露混合物的问题，已经出现了各种策略，包括加权分位数和（weighted quantile sum，WQS）回归等方法，这些方法可以估计混合物成分的联合效应。
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（quantile g-computation）。这种方法结合了 WQS 回归的推断简单性和 g 计算的灵活性，g 计算是一种因果效应估计方法。我们使用模拟来研究分位数 g 计算和 WQS 回归是否可以在各种常见情况下准确而精确地估计混合物的效应。
我们研究了分位数 g 计算和 WQS 回归的偏倚、置信区间（CI）覆盖度和偏差-方差权衡，以及这些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的非线性的影响。
分位数 g 计算与 WQS 回归不同，它允许在通常在流行病学研究中遇到的样本量下进行无偏推断，并具有适当的 CI 覆盖度，而无需满足 WQS 回归的假设。此外，WQS 回归可能会放大由于分析中遗漏了混合物的重要成分而导致的未测量混杂引起的偏差。
与检查在保持其他暴露不变的情况下单个暴露效应的推断方法不同，像分位数 g 计算这样可以估计混合物效应的方法对于理解潜在公共卫生行动的效应至关重要，这些行动会作用于暴露源。我们的方法可能有助于弥合流行病学分析与干预之间的差距，例如对工业排放或采矿过程、饮食变化或消费者行为变化等同时作用于多种暴露的法规。https://doi.org/10.1289/EHP5838.
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