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A Path-Specific Effect Approach to Mediation Analysis With Time-Varying Mediators and Time-to-Event Outcomes

Arce Domingo-Relloso1,2,3, Yuchen Zhang1, Ziqing Wang1

  • 1Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York, USA.

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Summary
This summary is machine-generated.

This study introduces a new method for survival analysis, accounting for competing risks in longitudinal mediation. The approach clarifies how exposures impact outcomes and competing events, improving causal inference.

Keywords:
competing risksg‐formulalongitudinal datamediation analysispath‐specific effectssurvival analysis

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Causal Inference

Background:

  • Survival analysis often yields biased estimates when competing events are ignored.
  • Existing mediation analysis methods do not adequately address non-separable exposures with non-terminal outcomes and competing risks.

Purpose of the Study:

  • To propose a novel framework for longitudinal mediation analysis that accounts for competing risks.
  • To extend the path-specific effects framework to handle situations with non-terminal mediators and outcomes.

Main Methods:

  • Developed a novel approach using the path-specific effects framework and the mediational g-formula.
  • Incorporated competing events as a nested mediator within the longitudinal mediator of interest.
  • Proposed a detailed algorithm for estimating path-specific effects in the presence of competing risks.

Main Results:

  • The proposed method provides a theoretical formulation and algorithmic description for causal mediation analysis with competing risks.
  • A simulation study demonstrated the validity and performance of the new approach.
  • Application to the Strong Heart Study revealed the mediating role of blood pressure on arsenic/cadmium and cardiovascular disease, considering death as a competing risk.

Conclusions:

  • The novel framework offers a transparent method for evaluating the impact of exposures on outcomes and competing risks in longitudinal studies.
  • This approach enhances causal inference in complex scenarios with competing events and time-to-event data.
  • The method is applicable to various fields, including environmental health and clinical research, for a more nuanced understanding of causal pathways.