Mediation analysis seeks to understand the mechanism by which a treatment affects an outcome. Count or zero-inflated count outcomes are common in many studies in which mediation analysis is of interest. For example, in dental studies, outcomes such as the number of decayed, missing and filled teeth are typically zero inflated. Existing mediation analysis approaches for count data often assume sequential ignorability of the mediator. This is often not plausible because the mediator is not randomized so unmeasured confounders are associated with the mediator and the outcome. We develop causal methods based on instrumental variable approaches for mediation analysis for count data possibly with many 0s that do not require the assumption of sequential ignorability. We first define the direct and indirect effect ratios for those data, and then we propose estimating equations and use empirical likelihood to estimate the direct and indirect effects consistently. A sensitivity analysis is proposed for violations of the instrumental variables exclusion restriction assumption. Simulation studies demonstrate that our method works well for different types of outcome under various settings. Our method is applied to a randomized dental caries prevention trial and a study of the effect of a massive flood in Bangladesh on children's diarrhoea.

中文翻译：

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无顺序可忽略性的计数和零膨胀计数数据的中介分析及其在牙科研究中的应用。

调解分析试图了解治疗影响结果的机制。计数或零膨胀计数结果在许多涉及中介分析的研究中很常见。例如，在牙科研究中，诸如腐烂，缺失和填充的牙齿数量之类的结果通常为零膨胀。现有的计数数据调解分析方法通常假定调解器是顺序可忽略的。这通常是不合理的，因为调解人不是随机分配的，因此无法衡量的混杂因素与调解人和结果相关。我们开发了基于工具变量方法的因果方法来进行调解分析，以计算计数数据，可能具有多个0，而无需假设连续可忽略性。我们首先定义这些数据的直接和间接影响比，然后我们提出估计方程，并使用经验似然来一致地估计直接和间接影响。针对违反工具变量排除限制假设的情况，建议进行敏感性分析。仿真研究表明，我们的方法在各种情况下对于不同类型的结果都适用。我们的方法应用于预防龋齿的随机试验，以及孟加拉国大规模洪水对儿童腹泻的影响研究。