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Multiply robust estimation of natural indirect effects with multiple ordered mediators.

An-Shun Tai1, Sheng-Hsuan Lin2

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Summary

This study introduces multiply robust estimators for natural indirect effects (NIEs) in multiple mediation analysis, offering greater protection against model misspecification. Simulations and a liver disease example demonstrate their effectiveness compared to existing methods.

Keywords:
causal mediation analysismultiple ordered mediatorsmultiply robust estimationnatural indirect effects

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Area of Science:

  • Causal inference
  • Statistical methodology
  • Biostatistics

Background:

  • Multiple mediation analysis is crucial for understanding complex causal pathways with multiple mediators.
  • Existing methods like G-computation and inverse-probability-weighting for natural indirect effects (NIEs) are susceptible to model misspecification.
  • A robust method for multiple mediation analysis, particularly with ordered mediators, is currently lacking.

Purpose of the Study:

  • To propose a novel method using multiply robust estimators for natural indirect effects (NIEs) in the presence of multiple ordered mediators.
  • To develop estimators that are robust to model misspecification, a key limitation of current approaches.
  • To provide a statistically sound and reliable tool for complex causal pathway analysis.

Main Methods:

  • Development of multiply robust estimators for natural indirect effects (NIEs).
  • Theoretical analysis demonstrating consistency and asymptotic normality under regular conditions.
  • Simulation studies to compare finite-sample properties with existing methods.
  • Application to a real-world dataset on liver disease patients in Taiwan.

Main Results:

  • The proposed multiply robust estimators demonstrate robustness against model misspecification.
  • The estimators are shown to be consistent and asymptotically normal.
  • Simulation results indicate favorable finite-sample properties compared to existing methods.
  • The method was successfully applied to analyze mediating roles in liver disease progression.

Conclusions:

  • The proposed multiply robust estimators offer a more reliable approach to multiple mediation analysis, particularly when model specification is uncertain.
  • This method enhances causal inference by providing robust estimates of natural indirect effects (NIEs).
  • The availability of the R package "MedMR" facilitates the practical application of this advanced statistical technique.