A random subset implementation of weighted quantile sum (WQSRS) regression for analysis of high-dimensional mixtures
A random subset implementation of weighted quantile sum (WQSRS) regression for analysis of high-dimensional mixtures
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DOI：
10.1080/03610918.2019.1577971
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发表时间：
2019-08-24
影响因子：
0.9
通讯作者：
Gennings, Chris
中科院分区：
文献类型：
--
作者：
Curtin, Paul;Kellogg, Joshua;Gennings, Chris
在这里，我们介绍了一种新的实现加权分位数和（WQS）回归，混合物分析的建模策略，它集成了一个随机子集算法的混合效应的估计。我们证明了这种方法（WQS（RS））在三个案例中的应用，混合物的大小从34到472个变量。在评估每种情况下，我们提供了详细的模拟研究，以表征WQS（RS）在不同情况下的灵敏度和特异性。我们的研究结果强调，WQS（RS）在评估不同高维背景下的混合效应方面是鲁棒有效的，在经验背景下的灵敏度和特异性分别约为73-75%和73- 89%。
Here we introduce a novel implementation of weighted quantile sum (WQS) regression, a modeling strategy for mixtures analyses, which integrates a random subset algorithm in the estimation of mixture effects. We demonstrate the application of this method (WQS(RS)) in three case examples, with mixtures varying in size from 34 to 472 variables. In evaluating each case, we provide detailed simulation studies to characterize the sensitivity and specificity of WQS(RS) in varying contexts. Our results emphasize that WQS(RS) is robustly effective in evaluating mixture effects in diverse high-dimensional contexts, yielding sensitivity and specificity in empirical contexts of approximately 73-75% and 73-89%, respectively.