存在非随机缺失的多重中介的中介效应分析
Mediation Analysis with Multiple Mediators Subject to Missing Not at Random
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中文总结 AI 辅助
本文针对存在非随机缺失的多重中介问题，建立因果中介效应的识别框架并开发估计方法，经模拟验证有效，应用于NHANES数据展示了方法的实用性。
中文摘要 AI 辅助
因果中介分析是揭示处理与结局之间关联的中介机制的关键工具。现有的多重中介中介分析方法通常假设观测完整或数据随机缺失，当中介变量值为非随机缺失（MNAR）时，这些方法可能会产生有偏估计。本文研究存在MNAR缺失的多重中介的因果中介效应的识别与估计。我们考虑一类广泛的MNAR机制，其中缺失可能取决于未观测的中介变量、处理、协变量和结局。在一系列日益通用的MNAR机制下，我们建立了可识别的自然直接效应和间接效应，有效将现有中介分析推广到处理不可忽略的缺失中介的情况。基于所提出的识别框架，我们开发了因果中介效应的估计程序，并通过模拟研究评估其有限样本性能。结果表明，在一系列缺失场景中，该方法表现令人满意。将其应用于美国国家健康与营养检查调查（NHANES）的数据，说明了所提方法在存在不可忽略缺失数据时研究中介路径的实用性。
英文摘要
Causal mediation analysis serves as a key tool for uncovering the mediating mechanisms linking treatments to outcomes. Existing methods for mediation analysis with multiple mediators typically assume complete observations or missing-at-random and may yield biased estimation when mediator values are missing not at random (MNAR). This paper studies the identification and estimation of causal mediation effects with multiple mediators subject to MNAR missingness. We consider a broad class of MNAR mechanisms in which missingness may depend on unobserved mediators, treatment, covariates, and outcomes. Under a series of increasingly general MNAR mechanisms, we establish identified natural direct and indirect effects, effectively generalizing existing mediation analysis to handle nonignorable missing mediators. Based on the proposed identification framework, we develop estimation procedures for causal mediation effects and evaluate their finite-sample performance through simulation studies. The results demonstrate satisfactory performance across a range of missingness scenarios. An application to data from the National Health and Nutrition Examination Survey(NHANES) illustrates the practical utility of the proposed methodology for investigating mediation pathways in the presence of nonignorable missing data.
