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arXiv · 2305.06651

Robust Inference for Causal Mediation Analysis of Recurrent Event Data

Yan-Lin Chen·Yan-Hong Chen·Pei-Fang Su·Huang-Tz Ou·An-Shun Tai
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Abstract

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.

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Yan-Lin Chen, Yan-Hong Chen, Pei-Fang Su, Huang-Tz Ou, An-Shun Tai. 2023-05-11. Robust Inference for Causal Mediation Analysis of Recurrent Event Data. https://doi.org/10.1002/sim.10118

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