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

Tat-Thang Vo (), Hilary Davies-Kershaw, Ruth Hackett and Stijn Vansteelandt
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Tat-Thang Vo: Ghent University
Hilary Davies-Kershaw: London School of Hygiene and Tropical Medicine
Ruth Hackett: King’s College London
Stijn Vansteelandt: Ghent University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 3, No 3, 380-400

Abstract: 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 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).

Keywords: Longitudinal mediation analysis; Natural effect model; Inverse weighting; Survival outcome (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-022-09555-7

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