Robust inference for causal mediation analysis of recurrent event data

• Published in: Statistics in medicine Vol. 43; no. 16; pp. 3020 - 3035
• Main Authors: Chen, Yan‐Lin, Chen, Yan‐Hong, Su, Pei‐Fang, Ou, Huang‐Tz, Tai, An‐Shun
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 20.07.2024; Wiley Subscription Services, Inc
• Subjects: Biomedical research; Cardiovascular disease; Cardiovascular Diseases; causal inference; Causality; Computer Simulation; Data Interpretation, Statistical; Humans; Hypoglycemic Agents - therapeutic use; inverse probability weighting; Mediation; Mediation Analysis; Models, Statistical; Recurrence; recurrent events; robust inference; triply robust estimation; robust inference; mediation analysis; inverse probability weighting; recurrent events; triply robust estimation; causal inference
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.10118

Recurrent events, including cardiovascular events, are commonly observed in biomedical studies. Understanding the effects of various treatments on recurrent events and investigating the underlying mediation mechanisms by which treatments may reduce the frequency of recurrent events are crucial tasks for researchers. 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, and 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.