Various methods have emerged for conducting mediation analyses with multiple correlated mediators, each with distinct strengths and limitations. However, a comparative evaluation of these methods is lacking, providing the motivation for this paper. This study examines six mediation analysis methods for multiple correlated mediators that provide insights to the contributors for health disparities. We assessed the performance of each method in identifying joint or path-specific mediation effects in the context of binary outcome variables varying mediator types and levels of residual correlation between mediators. Through comprehensive simulations, the performance of six methods in estimating joint and/or path-specific mediation effects was assessed rigorously using a variety of metrics including bias, mean squared error, coverage and width of the 95$\%$ confidence intervals. Subsequently, these methods were applied to the REasons for Geographic And Racial Differences in Stroke (REGARDS) study, where differing conclusions were obtained depending on the mediation method employed. This evaluation provides valuable guidance for researchers grappling with complex multi-mediator scenarios, enabling them to select an optimal mediation method for their research question and dataset.

翻译：针对多重相关中介变量的中介分析，已涌现出多种方法，各具优势与局限。然而，目前尚缺乏对这些方法的比较性评估，这构成了本文的研究动机。本研究考察了六种适用于多重相关中介变量的中介分析方法，这些方法有助于揭示健康差异的影响因素。我们评估了每种方法在识别联合或路径特异性中介效应时的性能，研究情境涉及二分类结局变量、不同类型的中介变量以及中介变量间不同程度的残差相关性。通过全面的模拟研究，我们使用偏差、均方误差、95$\%$置信区间的覆盖率和宽度等多种指标，严格评估了六种方法在估计联合和/或路径特异性中介效应时的表现。随后，将这些方法应用于“卒中地理与种族差异原因”（REGARDS）研究，发现采用不同的中介分析方法会得出不同的结论。本评估为处理复杂多中介场景的研究人员提供了宝贵指导，有助于他们根据具体研究问题和数据集选择最优的中介分析方法。