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.

翻译：中介分析是一种日益流行的统计方法，用于解释因果路径以指导干预措施。尽管方法学已有所发展，但针对存在过多零值的计数结局，仍缺乏稳健的中介分析方法。当前处理该问题的中介方法存在计算强度大、存在偏倚或解释性差的局限。为克服这些限制，我们提出了一种基于边际化零膨胀泊松（MZIP）模型与反事实中介分析框架的新型中介方法学，适用于零膨胀计数结局。该创新性工作可提供总体平均中介效应，其方差可通过delta方法快速估计。该方法进一步扩展至暴露-中介交互作用的情形。我们应用该方法探究糖尿病诊断能否解释医疗利用中的体重指数（BMI）差异，并通过模拟实验将所提MZIP方法与现有零膨胀及泊松方法进行比较，以检验模型性能。结果发现，与替代方法相比，本文方法在最小化偏倚和计算时间的同时，保持了直观的可解释性。