Estimation of causal mediation effects for a dichotomous outcome in multiple-mediator models using the mediation formula.
Estimation of causal mediation effects for a dichotomous outcome in multiple-mediator models using the mediation formula.
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DOI：
10.1002/sim.5830
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发表时间：
2013-10-30
影响因子：
2
通讯作者：
Albert, Jeffrey M.
中科院分区：
文献类型：
--
作者：
Wang, Wei;Nelson, Suchitra;Albert, Jeffrey M.
关键词：
中介变量是暴露和结果之间因果路径中的中间变量。中介分析调查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，我们考虑的中介效果的估计时，结果是二元和多个不同类型的中介存在。我们给出了一个精确的定义，总的调解效果，以及分解的调解效果，通过个人或一组调解员使用潜在的结果框架。我们制定了一个模型的联合分布（概率正态分布）使用连续的潜变量的任何二元介质之间的相关性考虑到多个介质。在此参数模型的基础上，提出了一种估计总中介效应和分解中介效应的中介公式方法。通过个体或中介子集合来估计中介效应需要一个涉及多个反事实联合分布的假设。我们进行了模拟研究，证明了低偏差的中介效应估计的两个中介模型与各种组合的中介类型。研究结果还表明，功率检测非零的总调解效果增加的两个调解员之间的相关系数增加，而功率为个人调解效果达到最大时，调解员是不相关的。我们说明了我们的方法，将其应用到一个回顾性队列研究的龋齿在青少年低和高的社会经济地位。进行敏感性分析，以评估当没有不可测量的中介结果混杂因素的假设被违反时，关于中介效应的结论的稳健性。
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 non-zero total mediation effect increases as the correlation coefficient between two mediators increases, while 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.
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