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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Mediation analysis is vital for understanding causal mechanisms.
  • Unmeasured confounding of the mediator-outcome relationship can invalidate direct and indirect effect estimates.
  • Existing sensitivity analysis methods for unmeasured confounding are not always utilized due to interpretability challenges.

Purpose of the Study:

  • To develop a more intuitive parameterization for sensitivity analysis in mediation studies.
  • To assess the performance of an alternative parameterization for bounding unmeasured mediator-outcome confounding.

Main Methods:

  • Conducted an exhaustive numerical search over simulated mediation effects.
  • Calculated the proportion of scenarios where a bound for unmeasured confounding held under an alternative parameterization.
  • Focused on describing confounding strength via the confounder-mediator relationship.

Main Results:

  • The proposed bound for unmeasured confounding held in over 99% of simulated scenarios.
  • The alternative parameterization, focusing on the confounder-mediator relationship, proved effective.
  • This approach offers a more straightforward way to assess potential bias.

Conclusions:

  • A straightforward sensitivity analysis method for unmeasured confounding in mediation analysis is presented.
  • This method uses a parameterization based on the confounder-mediator and confounder-outcome relationships.
  • It allows researchers to assess the bias required to explain away direct or indirect effects.