  Mediation analysis is commonly used in epidemiological research, but guidance
is lacking on how multivariable missing data should be dealt with in these
analyses. Multiple imputation (MI) is a widely used approach, but questions
remain regarding impact of missingness mechanism, how to ensure imputation
model compatibility and approaches to variance estimation. To address these
gaps, we conducted a simulation study based on the Victorian Adolescent Health
Cohort Study. We considered six missingness mechanisms, involving varying
assumptions regarding the influence of outcome and/or mediator on missingness
in key variables. We compared the performance of complete-case analysis, seven
MI approaches, differing in how the imputation model was tailored, and a
"substantive model compatible" MI approach. We evaluated both the MI-Boot (MI,
then bootstrap) and Boot-MI (bootstrap, then MI) approaches to variance
estimation. Results showed that when the mediator and/or outcome influenced
their own missingness, there was large bias in effect estimates, while for
other mechanisms appropriate MI approaches yielded approximately unbiased
estimates. Beyond incorporating all analysis variables in the imputation model,
how MI was tailored for compatibility with mediation analysis did not greatly
impact point estimation bias. BootMI returned variance estimates with smaller
bias than MIBoot, especially in the presence of incompatibility.
