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

Author

Listed:
  • Tat-Thang Vo

    (Ghent University
    University of Pennsylvania)

  • Hilary Davies-Kershaw

    (London School of Hygiene and Tropical Medicine)

  • Ruth Hackett

    (King’s College London)

  • Stijn Vansteelandt

    (Ghent University
    London School of Hygiene and Tropical Medicine)

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).

Suggested Citation

  • Tat-Thang Vo & Hilary Davies-Kershaw & Ruth Hackett & Stijn Vansteelandt, 2022. "Longitudinal mediation analysis of time-to-event endpoints in the presence of competing risks," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 380-400, July.
    • Handle: RePEc:spr:lifeda:v:28:y:2022:i:3:d:10.1007_s10985-022-09555-7
    DOI: 10.1007/s10985-022-09555-7
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    References listed on IDEAS

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    1. Steen, Johan & Loeys, Tom & Moerkerke, Beatrijs & Vansteelandt, Stijn, 2017. "medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i11).
    2. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    3. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    4. Zheng Wenjing & van der Laan Mark, 2017. "Longitudinal Mediation Analysis with Time-varying Mediators and Exposures, with Application to Survival Outcomes," Journal of Causal Inference, De Gruyter, vol. 5(2), pages 1-24, September.
    5. Torben Martinussen & Stijn Vansteelandt & Per Kragh Andersen, 2020. "Subtleties in the interpretation of hazard contrasts," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 833-855, October.
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