Posttreatment confounding in causal mediation studies: A cutting-edge problem and a novel solution via sensitivity...

Area of Science:

• Causal Inference
• Biostatistics
• Epidemiology

Background:

• Posttreatment confounding is a common challenge in causal mediation analysis, particularly with treatment-by-mediator interactions.
• Existing methods struggle to adjust for posttreatment confounders due to incomplete counterfactual data.

Purpose of the Study:

• To propose a new sensitivity analysis strategy for handling posttreatment confounding in causal mediation analysis.
• To incorporate this strategy into weighting-based methods for estimating indirect and direct effects.

Main Methods:

• Develop a method to estimate the conditional distribution of a posttreatment confounder under counterfactual treatment.
• Utilize sensitivity analysis to generate bounds for natural indirect and direct effects.
• Implement the strategy using imputation or integration for binary or continuous confounders.

Main Results:

• The proposed sensitivity analysis provides bounds for indirect and direct effects, accounting for posttreatment confounding.
• Simulation results highlight the strengths and limitations of the new approach.
• Reanalysis of NEWWS data shows initial findings are sensitive to omitted posttreatment confounding.

Conclusions:

• The new sensitivity analysis strategy effectively addresses posttreatment confounding in causal mediation.
• This method enhances the reliability of estimating indirect and direct effects in the presence of treatment-mediator interactions.
• Researchers should consider this approach to assess the impact of potential posttreatment confounding.