Longitudinal mediation analysis of time-to-event endpoints in the presence of competing risks

TL;DR

This paper develops a new causal modeling approach for analyzing how mediators influence time-to-event outcomes with competing risks, demonstrated through a study on hearing loss, loneliness, and dementia.

Contribution

It introduces natural effect proportional hazard models with inverse probability weighting to identify and estimate mediation effects in complex survival data.

Findings

Little evidence that loneliness mediates hearing loss impact on dementia

Method effectively handles competing risks and time-dependent confounders

Provides a framework for causal mediation analysis in survival settings

Abstract

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 natural effect proportional (cause-specific) hazard models. These extend marginal structural proportional (cause-specific) hazard models to enable effect decomposition. We show that under certain causal 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 efect of hearing loss on dementia is mediated through the feeling of loneliness, with a non-statistically significant indirect effect equal to 1.012 (hazard ratio (HR) scale; 95% confidence interval (CI) 0.986 to 1.053).