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Abstract:
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Mediation refers to the effect transmitted by mediators that intervene in the relationship between an exposure and a response variable. Mediation analysis has been broadly studied in many fields. However, it remains a challenge for researchers to differentiate indirect effect from multiple mediators, especially when the involving variables are of hierarchical levels. Yu et al. (2014) proposed general definitions of mediation effects that were consistent for all different types of response, exposure, or mediation variables. With these definitions, multiple mediators can be considered simultaneously, and the indirect effects carried by individual mediators can be separated from the total effect. We extend the definitions to the multilevel data sets, where multilevel additive models are adapted to model the variable relationships, based on which mediation effects can be calculated at different levels. Moreover, transformations on variables are allowed for potential nonlinear relationships. An R package, mlma, was created to carry out the proposed analysis. Simulations show that the proposed method can effectively differentiate and estimate mediation effects from different levels.
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