  Recurrent events, including cardiovascular events, are commonly observed in
biomedical studies. Researchers must understand the effects of various
treatments on recurrent events and investigate the underlying mediation
mechanisms by which treatments may reduce the frequency of recurrent events are
crucial. Although causal inference methods for recurrent event data have been
proposed, they cannot be used to assess mediation. This study proposed a novel
methodology of causal mediation analysis that accommodates recurrent outcomes
of interest in a given individual. A formal definition of causal estimands
(direct and indirect effects) within a counterfactual framework is given,
empirical expressions for these effects are identified. To estimate these
effects, a semiparametric estimator with triple robustness against model
misspecification was developed. The proposed methodology was demonstrated in a
real-world application. The method was applied to measure the effects of two
diabetes drugs on the recurrence of cardiovascular disease and to examine the
mediating role of kidney function in this process.
