Population-average mediation analysis for zero-inflated count outcomes

TL;DR

This paper introduces a new population-average mediation analysis method for zero-inflated count data, addressing computational and interpretability issues of existing approaches, and demonstrates its effectiveness through simulations and real data application.

Contribution

The paper develops a novel mediation methodology using the marginalized zero-inflated Poisson model and counterfactual approach, enabling rapid variance estimation and handling exposure-mediator interactions.

Findings

Proposed method reduces bias and computation time.

Method allows straightforward interpretation of mediation effects.

Simulation studies show improved performance over existing methods.

Abstract

Mediation analysis is an increasingly popular statistical method for explaining causal pathways to inform intervention. While methods have increased, there is still a dearth of robust mediation methods for count outcomes with excess zeroes. Current mediation methods addressing this issue are computationally intensive, biased, or challenging to interpret. To overcome these limitations, we propose a new mediation methodology for zero-inflated count outcomes using the marginalized zero-inflated Poisson (MZIP) model and the counterfactual approach to mediation. This novel work gives population-average mediation effects whose variance can be estimated rapidly via delta method. This methodology is extended to cases with exposure-mediator interactions. We apply this novel methodology to explore if diabetes diagnosis can explain BMI differences in healthcare utilization and test model performance via simulations comparing the proposed MZIP method to existing zero-inflated and Poisson methods. We find that our proposed method minimizes bias and computation time compared to alternative approaches while allowing for straight-forward interpretations.