The Methodology Center and The College of Health and Human Development, The Pennsylvania State University, State College, PA 16801, USA.
Nurs Res. 2012 May-Jun;61(3):224-30. doi: 10.1097/NNR.0b013e318254165c.
Assessing mediation is important because most interventions are designed to affect an intermediate variable or mediator; this mediator, in turn, is hypothesized to affect outcome behaviors. Although there may be randomization to the intervention, randomization to levels of the mediator is not generally possible. Therefore, drawing causal inferences about the effect of the mediator on the outcome is not straightforward.
The aim of this study was to introduce an approach to causal mediation analysis using the potential outcomes framework for causal inference and then discuss this approach in terms of the scientific questions addressed and the assumptions needed for identifying and estimating the effects.
The approach is illustrated using data from the Criminal Justice Drug Abuse Treatment Studies: Reducing Risky Relationships-HIV intervention implemented with 243 incarcerated women re-entering the community. The intervention was designed to affect various mediators at 30 days postintervention, including risky relationship thoughts, beliefs, and attitudes, which were then hypothesized to affect engagement in risky sexual behaviors, such as unprotected sex, at 90 days postintervention.
Using propensity score weighting, we found the intervention resulted in a significant decrease in risky relationship thoughts (-0.529, p = .03) and risky relationship thoughts resulted in an increase in unprotected sex (0.447, p < .001). However, the direct effect of the intervention on unprotected sex was not significant (0.388, p = .479).
By reducing bias, propensity score models improve the accuracy of statistical analysis of interventions with mediators and allow researchers to determine not only whether their intervention works but also how it works.
评估中介作用很重要，因为大多数干预措施旨在影响中间变量或中介变量；而这个中介变量又被假设会影响结果行为。尽管可以对干预措施进行随机分组，但通常无法对中介变量的水平进行随机分组。因此，直接对中介变量对结果的影响进行因果推断并不简单。
本研究旨在介绍一种使用潜在结果框架进行因果推断的因果中介分析方法，然后根据所解决的科学问题和识别和估计效应所需的假设来讨论这种方法。
该方法使用 243 名重新进入社区的被监禁女性参与的刑事司法药物滥用治疗研究：降低风险关系 -HIV 干预的数据进行说明。该干预措施旨在影响 30 天后的各种中介变量，包括风险关系的想法、信念和态度，这些中介变量随后被假设会影响 90 天后的风险性行为，如无保护的性行为。
通过倾向评分加权，我们发现干预措施导致风险关系思想显著减少（-0.529，p=0.03），风险关系思想导致无保护性行为增加（0.447，p<.001）。然而，干预对无保护性行为的直接影响并不显著（0.388，p=0.479）。
通过减少偏差，倾向评分模型提高了具有中介变量的干预措施统计分析的准确性，并使研究人员不仅能够确定他们的干预措施是否有效，还能够确定其作用方式。
