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首页> 外文期刊>Statistics in Biosciences >A Multivariate Generalized Linear Model Approach to Mediation Analysis and Application of Confidence Ellipses
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A Multivariate Generalized Linear Model Approach to Mediation Analysis and Application of Confidence Ellipses

机译：一种多变量通用线性模型方法探讨分析与信心椭圆的应用

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摘要

Mediation analysis evaluates the significance of an intermediate variable on the causal pathway between an exposure and an outcome. One commonly utilized test for mediation involves evaluation of counterfactual effects, estimated from separate regression models, corresponding to a composite null hypothesis. However, the “compositeness” of this null hypothesis is not commonly acknowledged and accounted for in mediation analyses. We describe a generalized multivariate approach in which these separate regression models are fit simultaneously in a single parsimonious model. This multivariate modeling approach can reproduce standard mediation analysis and has notable advantages over separate regression models, including the ability to combine distributions in the exponential family with any link functions and perform likelihood-based tests of some relevant hypotheses using existing software. We propose the use of a novel visual representation of confidence intervals of the two estimates for the indirect path with the use of a confidence ellipse. The calculation of the confidence ellipse is facilitated by the multivariate approach, can test the components of the composite null hypothesis under a single experiment-wise type I error rate, and does not require estimation of the standard error of the product of coefficients from two separate regressions. This method is illustrated using three examples. The first compares results between the multivariate method and separate regression models. The second example illustrates the proposed methods in the presence of an exposure–mediator interaction, missing data and confounding, and the third example utilizes these proposed methods for an outcome and mediator with negative binomial distributions.
机译：调解分析评估中间变量在暴露和结果之间的因果途径上的重要性。一个常用的调解测试涉及评估来自单独的回归模型的反复性效果，对应于复合无效假设。然而，在调解分析中，不一定地确认并占该无效假设的“合并性”。我们描述了一种广义多变量方法，其中这些单独的回归模型在单个定义模型中同时适合。这种多变量建模方法可以重现标准调解分析，并在单独的回归模型中具有显着的优势，包括将指数族中的分布与任何链接功能相结合，并使用现有软件执行一些相关假设的基于可能性的测试。我们建议使用与使用信心椭圆的间接路径的两个估计的置信区间的新视觉表现。通过多变量方法促进了置信椭圆的计算，可以在单个实验中的I误码率下测试复合无效假设的组件，并且不需要从两个单独分开的系数产品的标准误差估计回归。使用三个例子来示出该方法。第一个比较多元方法与单独回归模型之间的结果。第二个例子示出了存在曝光介体相互作用，缺失数据和混杂性存在的所提出的方法，第三实施例利用这些提出的方法进行负二项份分布的结果和介体。

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