AI 中文总结
该研究针对多中介且结局为二分类的情形，提出基于回归的统一因果中介分析框架，推导因果效应闭式表达式并开发似然推断方法，通过实证应用验证其有效性。
AI 中文摘要
中介分析旨在探究治疗效应是否部分通过治疗与主要结局间因果路径上的一个或多个中介传递。然而，多个可能存在依赖关系的中介的存在带来了重大挑战，尤其当结局为二分类、中介测量尺度不同且存在交互作用时。我们考虑具有二分类治疗和二分类结局的因果中介分析，在相对风险尺度上定义自然直接、间接和总效应，从而避免与不可折叠效应度量相关的解释困难。在顺序可忽略性假设下，我们开发了一个统一的基于回归的框架，该框架可容纳多个连续、二分类或混合类型的中介。所提框架考虑了中介间的依赖关系，且允许存在暴露-中介及中介-中介交互作用。我们推导了不同中介设置下因果效应的闭式表达式，并开发了一种基于似然的推断程序，用于估计因果效应并量化其不确定性。该方法通过两个实证应用进行了说明。
英文摘要
Mediation analysis investigates whether part of the treatment effect is channelled through one or more mediators along the causal pathway between the treatment and the primary outcome. However, the presence of multiple, potentially dependent mediators raises substantial challenges, particularly when the outcome is binary, the mediators are measured on different scales, and interactions are present. We consider on causal mediation analysis with a binary treatment and a binary outcome, defining natural direct, indirect, and total effects on the relative-risk scale, thereby avoiding the interpretational difficulties associated with non-collapsible effect measures. Under a sequential ignorability assumption, we develop a unified regression-based framework that accommodates multiple continuous, binary, or mixed mediators. The proposed framework accounts for dependence among mediators and allows for both exposure-mediator and mediator-mediator interactions. We derive closed-form expressions for the causal effects across the different mediator settings and develop a likelihood-based inference procedure for estimating the causal effects and quantifying their uncertainty. The methodology is illustrated through two empirical applications.
