Empirical studies often demonstrate multiple causal mechanisms potentially involving simultaneous or causally related mediators. However, researchers often use simple mediation models to understand the processes because they do not or cannot measure other theoretically relevant mediators. In such cases, another potentially relevant but unobserved mediator potentially confounds the observed mediator, thereby biasing the estimated direct and indirect effects associated with the observed mediator and threatening corresponding inferences. Additionally, researchers may not know the extent to which their measures are reliable, and accordingly, measurement error may bias estimated effects and mislead statistical inferences. Given these threats, we explore how the omission of an unobserved mediator and/or using variables with measurement error biases estimates and affects inferences associated with the observed mediator. Then, building off Frank's impact threshold for a confounding variable (ITCV), we propose a correlation-based sensitivity analysis. Lastly, we provide an R package ConMed to assess the robustness of mediation inferences given the omission of an unobserved, confounding mediator and/or measurement error. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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未观察到的混杂中介和测量误差如何影响估计的中介效应和相应的统计推断？引入用于敏感性分析的 R 包 ConMed。

实证研究经常证明多种因果机制可能涉及同时或因果相关的调解人。然而，研究人员经常使用简单的中介模型来理解这些过程，因为他们没有或不能衡量其他理论上相关的中介。在这种情况下，另一个潜在相关但未观察到的中介可能会混淆观察到的中介，从而使与观察到的中介相关的估计直接和间接影响产生偏差，并威胁到相应的推论。此外，研究人员可能不知道他们的测量在多大程度上是可靠的，因此，测量误差可能会使估计效果产生偏差并误导统计推断。鉴于这些威胁，我们探讨了遗漏未观察到的中介和/或使用具有测量误差偏差的变量如何估计和影响与观察到的中介相关的推论。然后，根据弗兰克对混杂变量 (ITCV) 的影响阈值，我们提出了一种基于相关性的敏感性分析。最后，我们提供了一个 R 包 ConMed 来评估在遗漏未观察到的混杂中介和/或测量误差的情况下中介推论的稳健性。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。我们提供了一个 R 包 ConMed 来评估中介推论的稳健性，因为它忽略了一个未观察到的、混杂的中介和/或测量误差。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。我们提供了一个 R 包 ConMed 来评估中介推论的稳健性，因为它忽略了一个未观察到的、混杂的中介和/或测量误差。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。