Mediation analysis with time varying exposures and mediators
Tyler J. VanderWeele, Eric J. Tchetgen Tchetgen
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2017 · Volume 79 · Issue 3
Abstract
We consider causal mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are time varying confounders affected by prior exposure and a mediator, natural direct and indirect effects are not identified. However, we define a randomized interventional analogue of natural direct and indirect effects that are identified in this setting. The formula that identifies these effects we refer to as the ‘mediationalg-formula’. When there is no mediation, the mediationalg-formula reduces to Robins's regularg-formula for longitudinal data. When there are no time varying confounders affected by prior exposure and mediator values, then the mediationalg-formula reduces to a longitudinal version of Pearl's mediation formula. However, the mediationalg-formula itself can accommodate both mediation and time varying confounders and constitutes a general approach to mediation analysis with time varying exposures and mediators.
Keywords
Counterfactual, Direct and indirect effect, Longitudinal data, Mediation, Pathway analysis, Time varying confounding
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