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Mediation analysis for common binary outcomes.

Sheila M Gaynor1, Joel Schwartz2,3, Xihong Lin1,4

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|September 27, 2018
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Summary
This summary is machine-generated.

This study introduces new mediation analysis methods for common diseases, improving causal inference for binary outcomes. The approach relaxes the rare disease assumption in logistic regression, offering more accurate natural direct and indirect effects.

Keywords:
causal inferencedichotomous responsemediationodds ratio

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Mediation analysis decomposes exposure effects into direct and indirect pathways.
  • Existing logistic regression methods for mediation analysis assume rare binary outcomes, limiting their application to common diseases.
  • Accurate causal inference is crucial for understanding disease mechanisms and developing targeted interventions.

Purpose of the Study:

  • To develop novel mediation analysis methods applicable to common binary outcomes, relaxing the rare disease assumption.
  • To provide closed-form expressions for natural direct and indirect effects on the odds ratio scale.
  • To extend mediation analysis to models with both continuous and binary mediators.

Main Methods:

  • Exploited the relationship between logit and probit models to extend mediation analysis for common binary outcomes.
  • Derived closed-form expressions for natural direct and indirect effects on the odds ratio scale.
  • Evaluated method performance through simulations and applied it to a real-world epidemiological study.

Main Results:

  • The proposed method demonstrates good performance for common binary outcomes in simulations.
  • The methods successfully identified potential DNA methylation mediators of smoking behavior on obstructed airway function in the Normative Aging Study.
  • Closed-form expressions provide a computationally efficient way to estimate effects.

Conclusions:

  • The developed mediation analysis methods effectively address the limitations of previous approaches for common binary outcomes.
  • This work enhances causal inference capabilities in epidemiology, particularly for prevalent diseases.
  • The application to the Normative Aging Study highlights the utility of these methods in identifying disease pathways.