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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Mediation analysis investigates indirect effects of exposures on outcomes via mediators.
  • Partially linear models are common in statistical analysis but pose challenges for mediation.
  • Existing methods may lack power or robustness in partially linear settings.

Purpose of the Study:

  • To develop robust G-estimators for direct and indirect mediation effects.
  • To propose novel GMM-based score tests for mediation and direct effects.
  • To improve statistical power and performance in partially linear mediation models.

Main Methods:

  • Utilized G-estimators for direct and indirect effects under no unmeasured confounding.
  • Developed a generalized methods of moments (GMM) framework for hypothesis testing.
  • Employed orthogonal estimation strategies for nuisance parameters.

Main Results:

  • Demonstrated consistent asymptotic normality for indirect and direct effects under specified conditions.
  • Proposed GMM-based tests show improved power and small sample performance over traditional tests.
  • New methods offer significant advantages, particularly under model misspecification.

Conclusions:

  • The proposed G-estimators and GMM-based tests provide a powerful and robust approach to mediation analysis in partially linear models.
  • These methods enhance statistical inference for direct and indirect effects.
  • The study offers practical tools, including an R package, for applying these advanced techniques.