Wang Wei, Nelson Suchitra, Albert Jeffrey M
Department of Epidemiology and Biostatistics, School of Medicine WG-37, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH 44106, USA.
Stat Med. 2013 Oct 30;32(24):4211-28. doi: 10.1002/sim.5830. Epub 2013 May 6.
Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets of mediators using the potential outcomes framework. We formulate a model of joint distribution (probit-normal) using continuous latent variables for any binary mediators to account for correlations among multiple mediators. A mediation formula approach is proposed to estimate the total mediation effect and decomposed mediation effects based on this parametric model. Estimation of mediation effects through individual or subsets of mediators requires an assumption involving the joint distribution of multiple counterfactuals. We conduct a simulation study that demonstrates low bias of mediation effect estimators for two-mediator models with various combinations of mediator types. The results also show that the power to detect a nonzero total mediation effect increases as the correlation coefficient between two mediators increases, whereas power for individual mediation effects reaches a maximum when the mediators are uncorrelated. We illustrate our approach by applying it to a retrospective cohort study of dental caries in adolescents with low and high socioeconomic status. Sensitivity analysis is performed to assess the robustness of conclusions regarding mediation effects when the assumption of no unmeasured mediator-outcome confounders is violated.
中介变量是暴露因素与结局之间因果路径中的中间变量。中介分析研究暴露效应通过这些变量发生的程度，从而揭示因果机制。在本文中，我们考虑当结局为二元变量且存在不同类型的多个中介变量时中介效应的估计。我们使用潜在结果框架给出了总中介效应以及通过单个或一组中介变量分解的中介效应的精确定义。对于任何二元中介变量，我们使用连续潜变量构建一个联合分布模型（probit-正态）以考虑多个中介变量之间的相关性。基于这个参数模型，我们提出了一种中介公式方法来估计总中介效应和分解的中介效应。通过单个或中介变量子集估计中介效应需要一个涉及多个反事实联合分布的假设。我们进行了一项模拟研究，结果表明对于具有不同中介变量类型组合的双中介变量模型，中介效应估计量的偏差较小。结果还表明，检测非零总中介效应的功效随着两个中介变量之间的相关系数增加而增加，而当两个中介变量不相关时，单个中介效应的功效达到最大值。我们将我们的方法应用于一项关于社会经济地位低和高的青少年龋齿的回顾性队列研究来进行说明。当未测量的中介变量 - 结局混杂因素的假设被违反时，进行敏感性分析以评估关于中介效应结论的稳健性。
