This proposal is motivated by an analysis of the English Longitudinal Study of Ageing (ELSA), which aims to investigate the role of loneliness in explaining the negative impact of hearing loss on dementia. The methodological challenges that complicate this mediation analysis include the use of a time-to-event endpoint subject to competing risks, as well as the presence of feedback relationships between the mediator and confounders that are both repeatedly measured over time. To account for these challenges, we introduce path-specific effect proportional (cause-specific) hazard models. These extend marginal structural proportional (cause-specific) hazard models to enable effect decomposition on either the cause-specific hazard ratio scale or the cumulative incidence function scale. We show that under certain ignorability assumptions, the path-specific direct and indirect effects indexing this model are identifiable from the observed data. We next propose an inverse probability weighting approach to estimate these effects. On the ELSA data, this approach reveals little evidence that the total effect of hearing loss on dementia is mediated through the feeling of loneliness, with a non-statistically significant indirect effect equal to 1.01 (hazard ratio (HR) scale; 95% confidence interval (CI) 0.99 to 1.05).

这项提议的动机是对英国老龄化纵向研究 (ELSA) 的分析，该研究旨在调查孤独在解释听力损失对痴呆症的负面影响方面的作用。使这种中介分析复杂化的方法挑战包括使用受竞争风险影响的事件发生时间终点，以及中介和混杂因素之间存在的反馈关系，这些关系都随着时间的推移而反复测量。为了解决这些挑战，我们引入了特定路径的效果比例（特定原因）风险模型。这些扩展了边际结构比例（特定原因）风险模型，以实现对特定原因风险比规模或累积发生率函数规模的影响分解。我们表明，在某些可忽略性假设下，索引该模型的路径特定的直接和间接影响可以从观察到的数据中识别出来。接下来，我们提出了一种逆概率加权方法来估计这些影响。在 ELSA 数据中，这种方法几乎没有证据表明听力损失对痴呆的总体影响是通过孤独感介导的，非统计显着的间接影响等于 1.01（风险比 (HR) 量表；95% 置信区间(CI) 0.99 至 1.05)。