Estimating Causal Effects in Mediation Analysis using Propensity Scores.
Estimating Causal Effects in Mediation Analysis using Propensity Scores.
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DOI：
10.1080/10705511.2011.582001
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发表时间：
2011-01-01
期刊：
影响因子：
--
通讯作者：
Coffman DL
中科院分区：
文献类型：
--
作者：
Coffman DL
中介通常通过基于回归或结构方程建模(SEM)的方法进行评估，我们将其称为经典方法。这种方法依赖于这样的假设，即不存在同时影响中介M和结果Y的混杂因素。如果个体被随机分配到M的水平，则该假设成立，但通常随机分配是不可能的。我们建议使用倾向分数来帮助消除当个体没有被随机分配到M的水平时可能导致的选择偏差。倾向分数是个体收到特定水平的M的概率。仿真研究的结果被提供来演示这种方法，被称为经典+倾向模型(C+PM)，证实了总体参数被恢复并且选择偏差被成功地处理。将其与不包括倾向分数的经典方法进行比较。用Logistic回归模型估计倾向性分数。如果所有混杂因素都包含在倾向模型中，则C+PM是无偏的。如果一些，但不是所有的混杂因素被包括在倾向模型中，那么C+PM估计是有偏差的，尽管不像经典方法那样严重(即，不包括倾向模型)。
Mediation is usually assessed by a regression-based or structural equation modeling (SEM) approach that we will refer to as the classical approach. This approach relies on the assumption that there are no confounders that influence both the mediator, M, and the outcome, Y. This assumption holds if individuals are randomly assigned to levels of M but generally random assignment is not possible. We propose the use of propensity scores to help remove the selection bias that may result when individuals are not randomly assigned to levels of M. The propensity score is the probability that an individual receives a particular level of M. Results from a simulation study are presented to demonstrate this approach, referred to as Classical + Propensity Model (C+PM), confirming that the population parameters are recovered and that selection bias is successfully dealt with. Comparisons are made to the classical approach that does not include propensity scores. Propensity scores were estimated by a logistic regression model. If all confounders are included in the propensity model, then the C+PM is unbiased. If some, but not all, of the confounders are included in the propensity model, then the C+PM estimates are biased although not as severely as the classical approach (i.e. no propensity model is included).
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影响因子：
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作者：
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通讯作者：
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DOI：
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影响因子：
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