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Mediation Analysis With Exposure-Mediator Interaction and Covariate Measurement Error Under the Additive Hazards

Ying Yan¹, Lingzhu Shen²

¹School of Mathematics, Sun Yat-sen University, Guangzhou, China.

Biometrical Journal. Biometrische Zeitschrift

|February 7, 2025

Summary

This study introduces a new method for causal mediation analysis with survival data, addressing measurement errors and exposure-mediator interactions. The approach provides accurate estimations of direct and indirect effects, improving reliability in biomedical research.

Keywords:

direct effect exposure–mediator interaction indirect effect measurement error survival analysis

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Area of Science:

Biostatistics
Epidemiology
Causal Inference

Background:

Causal mediation analysis examines exposure-outcome relationships via intermediate variables.
Mediation analysis with survival data is gaining research interest.
Existing methods often require precise measurements, which are frequently infeasible, and lack handling for exposure-mediator interactions.

Purpose of the Study:

To derive identification results for direct and indirect effects under the additive hazards model, accounting for exposure-mediator interactions.
To propose a corrected approach for measurement error in mediators and confounders.
To obtain consistent estimations of causal effects in the presence of measurement error and interactions.

Main Methods:

Developed identification strategies for direct and indirect effects within the additive hazards framework.
Proposed a statistical correction method to adjust for measurement errors in key variables.
Employed simulation studies and a real-world data analysis to validate the proposed methodology.

Main Results:

Successfully derived identification results for direct and indirect effects with exposure-mediator interaction under the additive hazards model.
The proposed corrected approach yields consistent estimations of direct and indirect effects, even with measurement errors.
Simulation studies and real data analysis demonstrated the practical utility and accuracy of the developed method.

Conclusions:

The study provides a robust framework for causal mediation analysis with survival data, accommodating complex scenarios like measurement error and interactions.
The proposed method enhances the reliability of causal effect estimation in epidemiological and biomedical research.
This work offers valuable tools for researchers dealing with imperfect data in mediation studies.