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Causal mediation analysis with a latent mediator.

Jeffrey M Albert1, Cuiyu Geng1, Suchitra Nelson2

  • 1Department of Epidemiology and Biostatistics, School of Medicine WG-82S, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.

Biometrical Journal. Biometrische Zeitschrift
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Summary

This study introduces a new method to estimate direct and indirect treatment effects using latent (unobserved) mediators in nonlinear models. The approach enhances causal inference in complex health research scenarios.

Keywords:
Factor analysisMeasurement errorMediation formulaMonte Carlo EM algorithmStructural equations model

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Assessing direct and indirect effects of exposures is crucial in health research.
  • Traditional mediation analysis often assumes observed intermediate variables (mediators).
  • Latent (unobserved) mediators present a challenge in causal inference.

Purpose of the Study:

  • To develop a method for estimating mediation effects with latent mediators in nonlinear models.
  • To extend causal mediation analysis within a potential outcomes framework.
  • To provide a robust estimation strategy for generalized structural equation models (GSEM).

Main Methods:

  • Utilized a potential outcomes framework and generalized structural equations modeling (GSEM).
  • Employed an approximate Monte Carlo EM algorithm for maximum-likelihood estimation of GSEM parameters.
  • Applied a mediation formula approach to estimate natural direct and indirect effects.
  • Incorporated sensitivity analysis to assess robustness to the sequential ignorability assumption.

Main Results:

  • Simulation studies demonstrated good performance of the proposed estimators under plausible conditions.
  • The method successfully estimated mediation effects through a latent oral health behavior in a study of adolescent dental caries.
  • The approach provides causally interpretable estimates for direct and indirect effects.

Conclusions:

  • The developed method enables causal mediation analysis with unobserved mediators in nonlinear GSEM.
  • This extends the applicability of mediation analysis to more complex, realistic health research settings.
  • The findings support the use of this method for investigating complex causal pathways in public health.