A0520
Title: Functional causal mediation analysis with zero-inflated count data
Authors: Yeying Zhu - University of Waterloo (Canada) [presenting]
Abstract: Mediation analysis is crucial for understanding how a treatment exerts effects on an outcome via an intermediate variable known as the mediator. Zero-inflated count outcomes and time-varying mediators are prevalent in fields such as biomedicine and biostatistics. To address the complex structure of the data, existing mediation analysis methodologies are extended by integrating a functional mediator in the context of zero-inflated count outcomes. The potential outcomes framework is employed to define the mediation effects of interest in this context and to provide the theoretical underpinning for the approach, including conditions for effect identification. Estimation and inference on the direct and indirect effects are performed by a quasi-Bayesian Monte Carlo approximation method, using the well-known mediation formula. The methods are applied to study gender disparity in the number of re-admission to ICU for patients in MIMIC-IV, an electronic health record database.
