Not accounting for competing events in survival analysis can lead to biased estimates, as individuals who die from other causes do not have the opportunity to develop the event of interest. Formal definitions and considerations for causal effects in the presence of competing risks have been published, but not for the mediation analysis setting. We propose, for the first time, an approach based on the path-specific effects framework to account for competing risks in longitudinal mediation analysis with time-to-event outcomes. We do so by considering the pathway through the competing event as another mediator, which is nested within our longitudinal mediator of interest. We provide a theoretical formulation and related definitions of the effects of interest based on the mediational g-formula, as well as a detailed description of the algorithm. We also present an application of our algorithm to data from the Strong Heart Study, a prospective cohort of American Indian adults. In this application, we evaluated the mediating role of the blood pressure trajectory (measured during three visits) on the association between arsenic and cadmium, in separate models, with time to cardiovascular disease, accounting for competing risks by death. Identifying the effects through different paths enables us to evaluate the impact of metals on the outcome of interest, as well as through competing risks, more transparently.

翻译：在生存分析中，若不考虑竞争事件，可能导致估计偏倚，因为死于其他原因的个体不再有机会发生目标事件。尽管已有研究在竞争风险存在的情况下提出了因果效应的正式定义与考量，但尚未将其扩展至中介分析场景。本文首次提出一种基于路径特异性效应框架的方法，用于处理生存时间结局的纵向中介分析中的竞争风险问题。具体而言，我们将通过竞争事件的路径视为另一个中介变量，该变量嵌套于我们关注的纵向中介变量之中。基于中介g公式，我们给出了目标效应的理论表述及相关定义，并对算法进行了详细描述。此外，我们将所提算法应用于强心研究的数据中——这是一项针对美国印第安成年人的前瞻性队列研究。在该应用中，我们分别评估了血压轨迹（通过三次访视测量）在砷与镉（分别建模）与心血管疾病发病时间关联中的中介作用，同时通过死亡事件考虑了竞争风险。通过识别不同路径的效应，我们能够更清晰地评估金属对目标结局的影响，以及通过竞争风险产生的间接影响。