Mediation analysis plays an essential role in uncovering the mechanisms by which an exposure influences an outcome through intermediate pathways. While methodological advances for single-mediator settings are well established, rigorous tools for handling multiple, sequentially ordered mediators remain underdeveloped. Such settings are common in applications like longitudinal cohort studies, where exposures operate through complex chains of mediators over time. In this paper, we establish a general framework for sequentially ordered mediators that enables the identification and formal decomposition of the total effect into component path-specific effects. We also develop estimation procedures for mediation estimands with both continuous and categorical outcomes. Furthermore, we introduce a new testing strategy to conduct inference using a studentized statistic combined with data-splitting. This approach achieves valid Type I error control under the composite null across diverse data-generating mechanisms. Through extensive simulations and applications to two large-scale empirical studies, we demonstrate that the proposed methodology provides reliable estimation, valid inference, and improved power for discovering novel mediation pathways.

翻译：中介分析在揭示暴露通过中间路径影响结局的机制中起着关键作用。尽管单一中介变量情境下的方法论进展已较为成熟，但应对多个顺序排列中介变量的严谨工具仍不完善。此类情境在纵向队列研究等应用中十分常见，暴露会通过随时间变化的复杂中介链发挥作用。本文建立了顺序中介变量的一般性框架，能够识别总效应并将其正式分解为各路径特定效应。我们还开发了针对连续型和分类型结局的中介估计量的估计方法。此外，我们引入了一种新的检验策略，通过学生化统计量结合数据分割进行推断。该方法在各种数据生成机制下，能够在复合零假设下实现有效的I类错误控制。通过大量模拟实验和两项大规模实证研究的应用，我们证明了所提方法能够提供可靠的估计、有效的推断，并在发现新型中介路径方面具有更优的检验功效。