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.

翻译：中介分析在流行病学研究中被广泛使用，但关于如何处理这些分析中的多变量缺失数据，目前缺乏指导。多重插补（MI）是一种广泛应用的方法，但关于缺失机制的影响、如何确保插补模型兼容性以及方差估计方法等问题仍有待探讨。为填补这些空白，我们基于维多利亚州青少年健康队列研究开展了一项模拟研究。我们考虑了六种缺失机制，涉及结果变量和/或中介变量对关键变量缺失情况的不同影响假设。我们比较了完整病例分析、七种差异化的MI方法（通过调整插补模型构建方式）以及一种"实质性模型兼容"的MI方法的性能。我们同时评估了MI-Boot（先MI再自举法）和Boot-MI（先自举法再MI）两种方差估计方法。结果表明：当中介变量和/或结果变量影响其自身缺失时，效应估计存在较大偏差；而在其他机制下，合适的MI方法能提供近似无偏的估计。除了在插补模型中纳入所有分析变量外，MI方法针对中介分析兼容性的调整方式对点估计偏差影响不显著。与MI-Boot相比，Boot-MI返回的方差估计偏差更小，尤其是在存在不兼容性的情况下。