Sensitivity Analysis for Assumptions of General Mediation Analysis

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Communications in Statistics: Simulation and Computation


Mediation analysis is widely used to identify significant mediators and estimate the mediation (direct and indirect) effects in causal pathways between an exposure variable and a response variable. In mediation analysis, the mediation effect refers to the effect transmitted by mediator intervening the relationship between an exposure variable and a response variable. Traditional mediation analysis methods, such as the difference in the coefficient method, the product of the coefficient method, and counterfactual framework method, all require several key assumptions. Thus, the estimation of mediation effects can be biased when one or more assumptions are violated. In addition to the traditional mediation analysis methods, Yu et al. proposed a general mediation analysis method that can use general predictive models to estimate mediation effects of any types of exposure variable(s), mediators and outcome(s). However, whether this method relies on the assumptions for the traditional mediation analysis methods is unknown. In this paper, we perform series of simulation studies to investigate the impact of violation of assumptions on the estimation of mediation effects using Yu et al.’s mediation analysis method. We use the R package mma for all estimations. We find that three assumptions for traditional mediation analysis methods are also essential for Yu et al.’s method. This paper provides a pipeline for using simulations to evaluate the impact of the assumptions for the general mediation analysis.




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