无序贯可忽略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
Mediation Analysis for Count and Zero-Inflated Count Data without Sequential Ignorability and Its Application in Dental Studies
摘要：
中介分析试图理解治疗影响结果的机制。计数或零膨胀计数结果在许多研究中是常见的，其中中介分析是感兴趣的。例如，在牙科研究中，诸如龋齿、缺失和补牙的结果通常是零膨胀的。现有的中介分析计数数据的方法假设中介的连续性。这通常是不合理的，因为中介不是随机的，因此存在与中介和结果相关的不可测量的混杂因素。在本文中，我们开发的因果关系方法的基础上工具变量（IV）的方法中介分析计数数据可能有很多零，不需要假设的顺序可验证性。我们首先定义这些数据的直接和间接效应比，然后提出估计方程，并使用经验似然估计的直接和间接影响一致。一个敏感性分析提出了违反IV排除限制假设。仿真研究表明，我们的方法适用于不同类型的结果在不同的设置。我们的方法被应用到一个随机的龋齿预防试验和一项研究的影响，在孟加拉国的大规模洪水对儿童腹泻。
英文摘要：
Mediation analysis seeks to understand the mechanism by which a treatment affects an outcome. Count or zero-inflated count outcome are common in many studies in which mediation analysis is of interest. For example, in dental studies, outcomes such as decayed, missing and filled teeth are typically zero inflated. Existing mediation analysis approaches for count data assume sequential ignorability of the mediator. This is often not plausible because the mediator is not randomized so that there are unmeasured confounders associated with the mediator and the outcome. In this paper, we develop causal methods based on instrumental variable (IV) approaches for mediation analysis for count data possibly with a lot of zeros that do not require the assumption of sequential ignorability. We first define the direct and indirect effect ratios for those data, and then propose estimating equations and use empirical likelihood to estimate the direct and indirect effects consistently. A sensitivity analysis is proposed for violations of the IV exclusion restriction assumption. Simulation studies demonstrate that our method works well for different types of outcomes under different 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 diarrhea.
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