Mediation analyses play important roles in making causal inference in biomedical research to examine causal pathways that may be mediated by one or more intermediate variables (i.e., mediators). Although mediation frameworks have been well established such as counterfactual-outcomes (i.e., potential-outcomes) models and traditional linear mediation models, little effort has been devoted to dealing with mediators with zero-inflated structures due to challenges associated with excessive zeros. We develop a novel mediation modeling approach to address zero-inflated mediators containing true zeros and false zeros. The new approach can decompose the total mediation effect into two components induced by zero-inflated structures: the first component is attributable to the change in the mediator on its numerical scale which is a sum of two causal pathways and the second component is attributable only to its binary change from zero to a non-zero status. An extensive simulation study is conducted to assess the performance and it shows that the proposed approach outperforms existing standard causal mediation analysis approaches. We also showcase the application of the proposed approach to a real study in comparison with a standard causal mediation analysis approach.
翻译:调解分析在生物医学研究中作出因果推断以审查可能由一个或多个中间变量(即调解人)调解的因果途径方面发挥了重要作用。虽然调解框架已经确立,例如反事实结果(即潜在结果)模式和传统的线性调解模式等,但由于与零率过高有关的挑战,很少努力与零膨胀结构的调解人打交道。我们制定了新的调解模式办法,以解决零膨胀的调解人问题,其中包括真正的零和假零。新办法可以将总体调解效应分解为零膨胀结构引起的两个组成部分:第一个组成部分是由于调解人在数字规模上的变化,这是两个因果途径的总和,第二个组成部分只是由于调解人从零到非零状态的二进制变化。进行了广泛的模拟研究,以评估业绩,并表明拟议办法超越了现有的标准因果调解分析办法。我们还展示了拟议办法在实际研究中的应用情况,与标准的因果分析办法相比较。