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Handling Multivariable Missing Data in Causal Mediation Analysis Estimating Interventional Effects.

S Ghazaleh Dashti^1,2, Katherine J Lee^1,2, Julie A Simpson^3,4

¹From the Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.

Epidemiology (Cambridge, Mass.)

|April 1, 2025

Summary

This summary is machine-generated.

Multiple imputation methods provide unbiased estimates for interventional mediation effects in epidemiology when missing data mechanisms are absent. Bias emerges when confounders, mediators, or outcomes influence their own missingness.

Keywords:

Bootstrap Causal inference Causal mediation analysis Fully conditional specification Indirect effect Interventional effects Missing data Multiple imputation

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Area of Science:

Epidemiology
Biostatistics
Causal Inference

Background:

Interventional effects approach to causal mediation analysis is valuable for policy-relevant questions in epidemiology.
Multiple imputation is common for missing data but lacks guidance for interventional mediation analysis.
Key issues include missingness mechanisms, imputation model specification with g-computation, and variance estimation.

Purpose of the Study:

To provide guidance on best practices for multiple imputation in estimating interventional mediation effects.
To evaluate the impact of different missingness mechanisms on multiple imputation performance.
To compare variance estimation methods for interventional mediation effects.

Main Methods:

Simulations based on the Victorian Adolescent Health Cohort Study.
Considered seven missingness mechanisms involving confounders, mediators, and outcomes.
Compared complete case analysis, six multiple imputation approaches, and a substantive model compatible approach.
Evaluated MIBoot and BootMI for variance estimation.

Main Results:

Multiple imputation methods, when aligned with best practices, yielded approximately unbiased estimates without influence from missingness mechanisms.
Nonnegligible bias occurred when intermediate confounders, mediators, or outcomes influenced their own missingness.
The largest bias was observed when each variable influenced its own missingness.
BootMI demonstrated less bias in variance estimates compared to MIBoot.

Conclusions:

Multiple imputation is a viable method for interventional mediation analysis if missingness mechanisms are carefully considered.
Substantive model compatibility and appropriate imputation strategies are crucial for accurate estimation.
BootMI is a preferred method for variance estimation in this context.