Assessing mediation using marginal structural models in the presence of confounding and moderation.
Assessing mediation using marginal structural models in the presence of confounding and moderation.
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DOI：
10.1037/a0029311
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发表时间：
2012-12
影响因子：
7
通讯作者：
Zhong, Wei
中科院分区：
文献类型：
--
作者：
Coffman, Donna L.;Zhong, Wei
本文提出了边际结构模型（MSM）与反向倾向加权（IPW）评估调解。一般来说，个人不会被随机分配到调解人的级别。因此，中介和结果的混杂因素可能存在，限制了因果推理，这是中介分析的目标。回归调整或IPW都可以用来考虑混杂因素，但IPW有几个优点。即使对受治疗影响的中介物和结果的一个混杂因素进行回归调整，也会导致直接效应的估计有偏差（即，治疗对结果的影响，不通过调解人）。IPW的一个优点是，它可以适当地调整这种类型的混杂因素，假设没有不可测量的混杂因素。此外，我们说明，IPW估计提供了无偏估计的所有影响时，有一个基线的主持变量，与治疗相互作用，当有一个基线的主持变量，与调解人相互作用，当治疗与调解人相互作用。IPW估计还提供了存在非随机化治疗时所有效应的无偏估计。此外，为了检验中介，我们提出了一个检验无中介的零假设。最后，我们用一个经验数据集来说明这种方法，在这个数据集中，中介是连续的，这在心理学研究中是经常发生的。
This paper presents marginal structural models (MSMs) with inverse propensity weighting (IPW) for assessing mediation. Generally, individuals are not randomly assigned to levels of the mediator. Therefore, confounders of the mediator and outcome may exist that limit causal inferences, a goal of mediation analysis. Either regression adjustment or IPW can be used to take confounding into account, but IPW has several advantages. Regression adjustment of even one confounder of the mediator and outcome that has been influenced by treatment results in biased estimates of the direct effect (i.e., the effect of treatment on the outcome that does not go through the mediator). One advantage of IPW is that it can properly adjust for this type of confounding, assuming there are no unmeasured confounders. Further, we illustrate that IPW estimation provides unbiased estimates of all effects when there is a baseline moderator variable that interacts with the treatment, when there is a baseline moderator variable that interacts with the mediator, and when the treatment interacts with the mediator. IPW estimation also provides unbiased estimates of all effects in the presence of non-randomized treatments. In addition, for testing mediation we propose a test of the null hypothesis of no mediation. Finally, we illustrate this approach with an empirical data set in which the mediator is continuous, as is often the case in psychological research.
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影响因子：
7.6
作者：
BARON, RM;KENNY, DA
通讯作者：
KENNY, DA
DOI：
10.1080/10705511.2011.582001
发表时间：
2011-01-01
期刊：
Structural equation modeling : a multidisciplinary journal
影响因子：
--
作者：
Coffman DL
通讯作者：
Coffman DL
DOI：
10.1002/bs.3830190102
发表时间：
1974-01-01
期刊：
BEHAVIORAL SCIENCE
影响因子：
--
作者：
DEROGATIS, LR;LIPMAN, RS;COVI, L
通讯作者：
COVI, L
影响因子：
3.7
作者：
Angrist, JD;Imbens, GW;Rubin, DB
通讯作者：
Rubin, DB
影响因子：
2.3
作者：
Emsley, Richard;Dunn, Graham;White, Ian R.
通讯作者：
White, Ian R.