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A multivariate multiple third-variable effect analysis with an application to explore racial and ethnic disparities in obesity

机译:多变量多变量多元三种可变效应分析,申请探索肥胖症中的种族和民族差异

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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 nonlinear 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/multi-categorical 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.
机译:第三种可变效应是指从第三种变量(称为介质或混淆)到观察到的接触之间的关系和结果的干预效果。一般多种三维可变效应分析方法(TVEA)允许同时考虑多个介质/混淆器(MC),以及使用线性和非线性预测模型来估计MC效果。以前的研究发现,与非西班牙裔人群相比,黑人和西班牙裔白人与肥胖和相关的慢性疾病更加遭受更加不成比例的。在本文中,我们扩展了一般的TVEA来处理多元/多分类预测和多变量响应变量。我们为该扩展设计了算法和R包,并在NHANES数据上应用MMA,以识别MCS并量化同时肥胖和体重指数(BMI)的种族和族裔差异的间接效应。我们认为许多社会人口统计变量,个别因素和环境变量作为潜在的MCS,发现包括的变量解释了肥胖症和BMI中的一些种族/种族差异。

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