Mediation and moderation of treatment effects in randomised controlled trials of complex interventions
Mediation and moderation of treatment effects in randomised controlled trials of complex interventions
复制标题
DOI：
10.1177/0962280209105014
复制
发表时间：
2010-06-01
影响因子：
2.3
通讯作者：
White, Ian R.
中科院分区：
文献类型：
--
作者：
Emsley, Richard;Dunn, Graham;White, Ian R.
复杂的干预试验应该能够回答实用性和解释性的问题，以测试激励干预的理论，并帮助理解正在测试的临床问题的根本性质。其关键是估计治疗的直接效应和通过随机化后测量的中间变量起作用的间接效应。以心理治疗试验为例，复杂的干预措施，我们审查的统计方法，至关重要的直接和间接的影响，在中介和结果之间存在隐藏的混杂。我们回顾了历史文献的调解和适度的治疗效果。我们介绍了两种方法，从现有的因果推理文献，主要分层和结构平均模型，并展示了如何将这些可以应用在调解的情况下，讨论的方法和假设，实现基本因果模型的关键参数的可识别性。假设治疗（即随机化）对介导因子（即治疗相互作用的协变量）的影响存在基线协变量的修改，但协变量相互作用对这些治疗结局无直接影响，则需要使用工具变量方法。我们描述了如何通过随机化后变量进行调节，并将主要分层方法扩展到多组方法，并在主要分层中嵌套解释模型。我们用来自心理健康文献的随机试验的激励性例子来说明这种新方法。
Complex intervention trials should be able to answer both pragmatic and explanatory questions in order to test the theories motivating the intervention and help understand the underlying nature of the clinical problem being tested. Key to this is the estimation of direct effects of treatment and indirect effects acting through intermediate variables which are measured post-randomisation. Using psychological treatment trials as an example of complex interventions, we review statistical methods which crucially evaluate both direct and indirect effects in the presence of hidden confounding between mediator and outcome. We review the historical literature on mediation and moderation of treatment effects. We introduce two methods from within the existing causal inference literature, principal stratification and structural mean models, and demonstrate how these can be applied in a mediation context before discussing approaches and assumptions necessary for attaining identifiability of key parameters of the basic causal model. Assuming that there is modification by baseline covariates of the effect of treatment (i.e. randomisation) on the mediator (i.e. covariate by treatment interactions), but no direct effect on the outcome of these treatment by covariate interactions leads to the use of instrumental variable methods. We describe how moderation can occur through post-randomisation variables, and extend the principal stratification approach to multiple group methods with explanatory models nested within the principal strata. We illustrate the new methodology with motivating examples of randomised trials from the mental health literature.