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A Permutation Test-Based Approach to Strengthening Inference on the Effects of Environmental Mixtures: Comparison between Single-Index Analytic Methods
Environmental Health Perspectives ( IF 10.4 ) Pub Date : 2022-8-30
Drew B. Day, Sheela Sathyanarayana, Kaja Z. LeWinn, Catherine J. Karr, W. Alex Mason, Adam A. Szpiro

Abstract

Background:

Optimization of mixture analyses is critical to assess potential impacts of human exposures to multiple pollutants. Single-index regression methods quantify total mixture association and chemical component contributions. Single-index methods include several variants of quantile g-computation (QGC) and weighted quantile sum regression (WQSr), though each has limitations.

Objectives:

We developed a novel permutation test for WQSr and compared its performance to extant versions of WQSr and QGC in simulation studies and an analysis of prenatal phthalates and childhood cognition.

Methods:

WQSr uses ensemble nonlinear optimization to identify weights for mixture exposures in an index associated with the outcome in a prespecified direction, with ensembles based on bootstrap resampling (WQSBS) or random subsetting of exposures (WQSRS). Statistical significance can be assessed without splitting the data (Nosplit), by splitting the data into training and test sets (Split), by repeatedly holding out test sets (RH), or by using a novel permutation test (PT) to obtain a more accurate p-value. QGC instead provides a sum mixture coefficient and component coefficients with no constraints on direction. In simulations, we compared false positive rates (FPR) and power to detect true associations and accuracy in estimating mixture weights. We also estimated associations between prenatal phthalate mixtures and childhood IQ in the Conditions Affecting Neurocognitive Development and Learning in Early Childhood cohort using each method.

Results:

FPR was well controlled at 7% in all but the Nosplit WQSr variants. Among these methods, the WQSBS and WQSRS PT variants had the highest power (89%–98%), with lower power for QGC (85%–93%) and RH (60%–97%) or Split WQSr variants (40%–78%). WQSr methods estimated mixture weights 2–4 times more accurately than the QGC method. Coefficients for mixture associations with full scale IQ varied 3- to 4-fold across analytic methods.

Discussion:

WQSr paired with our novel permutation test best balanced power and false positive rate when assessing a mixture effect. As new methods develop, each should be examined for performance and applicability. https://doi.org/10.1289/EHP10570



中文翻译:

一种基于置换检验的方法来加强对环境混合物影响的推断:单指标分析方法之间的比较

摘要

背景:

混合物分析的优化对于评估人类暴露于多种污染物的潜在影响至关重要。单指数回归方法量化了总混合物关联和化学成分的贡献。单指数方法包括分位数 g 计算 (QGC) 和加权分位数和回归 (WQSr) 的几种变体,尽管每种方法都有局限性。

目标:

我们为 WQSr 开发了一种新的置换测试,并将其性能与现有版本的 WQSr 和 QGC 在模拟研究和产前邻苯二甲酸盐和儿童认知分析中进行了比较。

方法:

WQSr 使用集成非线性优化来识别与预定方向上的结果相关联的索引中的混合曝光权重,集成基于自举重采样 (WQSBS) 或曝光的随机子集 (WQSRS)。可以在不拆分数据 (Nosplit)、将数据拆分为训练集和测试集 (Split)、重复保留测试集 (RH) 或使用新的置换检验 (PT) 来评估统计显着性准确的p-价值。相反,QGC 提供了一个总和混合系数和分量系数,对方向没有限制。在模拟中,我们比较了误报率 (FPR) 和检测真实关联的能力以及估计混合权重的准确性。我们还使用每种方法在影响儿童早期神经认知发展和学习的条件中估计了产前邻苯二甲酸酯混合物与儿童智商之间的关联。

结果:

FPR 控制得很好7%除了 Nosplit WQSr 变体之外的所有变体。在这些方法中,WQSBS 和 WQSRS PT 变体的功率最高 (89%–98%),QGC (85%–93%) 和 RH (60%–97%) 或拆分 WQSr 变体 (40%) 的功率较低–78%)。WQSr 方法估计混合权重的准确度是 QGC 方法的 2-4 倍。与全尺度 IQ 的混合关联系数在分析方法中变化了 3 到 4 倍。

讨论:

在评估混合效应时,WQSr 与我们的新型排列测试最佳平衡功率和误报率配对。随着新方法的发展,应检查每种方法的性能和适用性。https://doi.org/10.1289/EHP10570

更新日期:2022-08-30
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