A Multivariate Multiple Third-Variable Effect Analysis with an Application to Explore Racial and Ethnic Disparities in Obesity.

A Multivariate Multiple Third-Variable Effect Analysis with an Application to Explore Racial and Ethnic Disparities in Obesity.
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DOI:
10.1080/02664763.2020.1738359
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发表时间:
2021
影响因子:
1.5
通讯作者:
Li B
Li B
中科院分区:
数学4区
文献类型:
--
作者:
Yu Q;Li B

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第三变量效应是指第三变量(称为中介变量或混杂因素)对暴露与结果之间观察到的关系的干预效应。一般的多重第三变量效应分析方法 (TVEA) 允许同时考虑多个中介/混杂因素 (MC),并使用线性和非线性预测模型来估计 MC 效应。此前的研究发现,与非西班牙裔白人相比,黑人和西班牙裔白人患肥胖症和相关慢性病的比例要高得多。在本文中,我们扩展了一般 TVEA 以处理多变量/多类别预测变量和多变量响应变量。我们为此扩展设计了算法和 R 包,并对 NHANES 数据应用 MMA 来识别 MC,并量化每个 MC 在同时解释肥胖和体重指数 (BMI) 方面的种族和民族差异时的间接影响。我们考虑了一些社会人口变量、个人​​因素和环境变量作为潜在的 MC,并发现肥胖和 BMI 的一些民族/种族差异可以通过所包含的变量来解释。
Third-Variable effect refers to the intervening effect from a third variable (called mediators or confounders) to the observed relationship between an exposure and an outcome. The general multiple third-variable effect analysis method (TVEA) allows consideration of multiple mediators/confounders (MC) simultaneously and the use of linear and non-linear predictive models for estimating MC effects. Previous studies have found that compared with non-Hispanic White population, Blacks and Hispanic Whites suffered disproportionally more with obesity and related chronic diseases. In this paper, we extend the general TVEA to deal with multivariate/multicategorical predictors and multivariate response variables. We designed algorithms and an R package for this extension and applied MMA on the NHANES data to identify MCs and quantify the indirect effect of each MC in explaining both racial and ethnic disparities in obesity and the body mass index (BMI) simultaneously. We considered a number of socio-demographic variables, individual factors, and environmental variables as potential MCs and found that some of the ethnic/racial differences in obesity and BMI were explained by the included variables.
DOI: 10.1002/sim.7977
发表时间: 2019-02-10
影响因子: 2
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