标题： 因果中介分析用于零膨胀混合中介变量 显示英文标题

标题： Causal Mediation Analysis for Zero-inflated Mixture Mediators

摘要： 因果中介分析是一种重要的统计工具，用于量化通过中间变量从原因到结果传递的效果。 在中介分析方法中存在一个空白，无法处理具有零膨胀、多峰性和异常行为的混合中介数据。 我们提出了一种创新的方法，从有限混合分布的角度对零膨胀混合中介进行建模，以灵活地捕捉此类中介数据。 考虑了多种数据类型来对这些中介进行建模，包括零膨胀对数正态混合、零膨胀泊松混合和零膨胀负二项混合。 推导出一种两部分的中介效应，以更好地理解中介变量中数值变化以及从0到1的二元变化对结果的影响。 通过期望最大化算法获得最大似然估计，以考虑未观察到的混合成员身份以及观察到的零是真实零还是虚假零。 通过模型选择准则选择混合成分的最佳数量。 所提出的方法在模拟研究中以及与标准中介分析方法相比，在神经科学研究中的应用中得到了验证。 显示英文摘要

摘要： Causal mediation analysis is an important statistical tool to quantify effects transmitted by intermediate variables from a cause to an outcome. There is a gap in mediation analysis methods to handle mixture mediator data that are zero-inflated with multi-modality and atypical behaviors. We propose an innovative way to model zero-inflated mixture mediators from the perspective of finite mixture distributions to flexibly capture such mediator data. Multiple data types are considered for modeling such mediators including the zero-inflated log-normal mixture, zero-inflated Poisson mixture and zero-inflated negative binomial mixture. A two-part mediation effect is derived to better understand effects on outcomes attributable to the numerical change as well as binary change from 0 to 1 in mediators. The maximum likelihood estimates are obtained by an expectation maximization algorithm to account for unobserved mixture membership and whether an observed zero is a true or false zero. The optimal number of mixture components are chosen by a model selection criterion. The performance of the proposed method is demonstrated in a simulation study and an application to a neuroscience study in comparison with standard mediation analysis methods.