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Sensitivity Plots for Confounder Bias in the Single Mediator Model

机译:单一调解人模型中混杂偏差的敏感度图

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Background: Causal inference continues to be a critical aspect of evaluation research. Recent research in causal inference for statistical mediation has focused on addressing the sequential ignorability assumption; specifically, that there is no confounding between the mediator and the outcome variable. Objectives: This article compares and contrasts three different methods for assessing sensitivity to confounding and describes the graphical depiction of these methods. Design: Two types of data were used to fully examine the plots for sensitivity analysis. The first type was generated data from a single mediator model with a confounder influencing both the mediator and the outcome variable. The second was data from an actual intervention study. With both types of data, situations are examined where confounding has a large effect and a small effect. Subjects: The nonsimulated data were from a large intervention study to decrease intentions to use steroids among high school football players. We demonstrate one situation where confounding is likely and another situation where confounding is unlikely. Conclusions: We discuss how these methods could be implemented in future mediation studies as well as the limitations and future directions for these methods.
机译:背景:因果推理仍然是评估研究的关键方面。统计调解因果推理的最新研究集中于解决顺序可忽略性假设。具体而言,调解人与结果变量之间没有混淆。目标:本文比较和对比了三种不同的评估混淆敏感性的方法,并描述了这些方法的图形描述。设计:两种类型的数据用于全面检查图以进行敏感性分析。第一种类型是从单个调解器模型生成的数据,其中混杂因素同时影响调解器和结果变量。第二个是来自实际干预研究的数据。使用这两种类型的数据,都会检查混杂影响较大和较小的情况。受试者:非模拟数据来自一项大型干预研究,目的是减少高中足球运动员使用类固醇的意图。我们演示了一种可能引起混淆的情况,以及另一种不可能发生混淆的情况。结论:我们讨论了如何在未来的调解研究中实施这些方法,以及这些方法的局限性和未来的方向。

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