AI 中文总结
本文针对含时依中介变量、时依混杂因素和时间-事件结局的场景，通过模拟与真实数据分析，对比差分法与参数化中介g-公式的性能，明确了差分法的适用条件与偏差来源，为该场景下的因果中介分析提供了实用替代方案。
AI 中文摘要
中介分析是一种强大的工具，可在正式统计框架中将处理效应分解为直接和间接成分，从而解释总处理效应。然而，将此类分析应用于存在时间-事件结局、时依中介变量和混杂因素的场景仍具挑战性。现有方法统计复杂、计算密集，且很少有用户友好的软件可用。差分法提供了一种简单替代方案，但它在该场景下的性能尚未得到系统评估。我们开展了模拟研究和真实数据分析，以填补文献中的这一空白。我们使用含时变协变量的Cox比例风险模型、Aalen加性风险模型和加速失效时间（AFT）模型，针对不同含时依中介变量和混杂因素的数据生成过程对比差分法，重点关注估计间接效应的偏差。我们以参数化中介g-公式作为基准对比。若模型设定正确，在不存在由处理直接导致的时依混杂因素的情况下，基于Aalen模型的差分法会产生无偏估计；在结局罕见时，基于Cox模型的差分法也会得到类似结果，但结局常见时则不然。由于可压缩性问题，基于AFT模型的差分法在几乎所有场景下都存在偏差。仅参数化中介g-公式在所有场景下均无偏。与专门方法相比，差分法需要额外的、往往不切实际的假设，例如处理对时依混杂因素不存在直接因果关系；然而，若这些假设成立，它可作为一种简单高效的替代方案使用。
英文摘要
Mediation analysis is a powerful tool to decompose treatment effects into direct and indirect components, enabling explanations of total treatment effects in a formal statistical framework. However, applying such analyses to settings with time-to-event outcomes, time-dependent mediators and confounders remains challenging. Existing methods are statistically complex, computationally intensive, and rarely available in user-friendly software. The difference method offers a simple alternative, but its performance in this setting has not been systematically evaluated. We conducted a simulation study and real-world data analysis to fill this gap in the literature. Using Cox proportional hazards, Aalen additive hazards, and accelerated failure time (AFT) models with time-varying covariates, the difference method was compared across different data generation processes with time-dependent mediators and confounders, focusing on bias in estimated indirect effects. The parametric mediational g-formula was used as benchmark comparator. If correctly specified, the Aalen model based difference method produced unbiased estimates in the absence of a time-dependent confounder that was directly caused by the treatment. Similar results were obtained when using the Cox model based difference with rare outcomes, but not with common outcomes. The AFT model based difference method was biased in almost all scenarios, due to collapsibility issues. Only the parametric mediational g-formula was unbiased in all scenarios. In contrast to specialized methods, the difference method requires additional, often unrealistic, assumptions, such as the absence of a direct causal relationship of the treatment on time-dependent confounders. If those assumptions hold, however, it may be used as a simple and efficient alternative.
