Causal Mediation Analysis: A Summary‐Data Mendelian Randomization Approach

Summary-data Mendelian randomization (MR), a widely used approach in causal inference, has recently attracted attention for improving causal mediation analysis. Two existing methods corresponding to the difference method and product method of linear mediation analysis have been developed to perform MR-based mediation analysis using the inverse-variance weighted method (MR-IVW). Despite these developments, there is still a need for more rigorous, efficient, and precise MR-based mediation methodologies. In this study, we develop summary-data MR-based frameworks for causal mediation analysis. We improve the accuracy, statistical efficiency and robustness of the existing MR-based mediation analysis by implementing novel variance estimators for the mediation effects, deriving rigorous procedures for statistical inference, and accounting for widespread pleiotropic effects. Specifically, we propose Diff-IVW and Prod-IVW to improve upon the existing methods and provide the pleiotropy-robust methods (Diff-Egger, Diff-Median, Prod-Egger, and Prod-Median), adapted from MR-Egger and MR-Median, to enhance the robustness of the MR-based mediation analysis. We conduct comprehensive simulation studies to compare the existing and proposed methods. The results show that the proposed methods, Diff-IVW and Prod-IVW, improve statistical efficiency and type I error control over the existing approaches. Although all IVW-based methods suffer from directional pleiotropy biases, the median-based methods (Diff-Median and Prod-Median) can mitigate such biases. The differences among the methods can lead to discrepant statistical conclusions as demonstrated in real data applications. Based on our simulation results, we recommend the three proposed methods in practice: Diff-IVW, Prod-IVW, and Prod-Median, which are complementary under various scenarios.