Department of Paediatrics, University of Melbourne, Melbourne, Australia.
Murdoch Children's Research Institute, Melbourne, Australia.
Stat Methods Med Res. 2021 Jun;30(6):1395-1412. doi: 10.1177/0962280221998409. Epub 2021 Mar 20.
Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the benefit of interventions in young adulthood for ameliorating the poorer mid-life psychosocial outcomes of adolescent self-harmers relative to their healthy peers. Two methodological challenges arise. First, mediation methods have hitherto mostly focused on the elusive task of discovering pathways, rather than on the evaluation of mediator interventions. Second, the complexity of such questions is invariably such that there are no well-defined mediator interventions (i.e. actual treatments, programs, etc.) for which data exist on the relevant populations, outcomes and time-spans of interest. Instead, researchers must rely on exposure (non-intervention) data, that is, on mediator measures such as depression symptoms for which the actual interventions that one might implement to alter them are not well defined. We propose a novel framework that addresses these challenges by defining mediation effects that map to a target trial of hypothetical interventions targeting multiple mediators for which we simulate the effects. Specifically, we specify a target trial addressing three policy-relevant questions, regarding the impacts of hypothetical interventions that would shift the mediators' distributions (separately under various interdependence assumptions, jointly or sequentially) to user-specified distributions that can be emulated with the observed data. We then define novel interventional effects that map to this trial, simulating shifts by setting mediators to random draws from those distributions. We show that estimation using a g-computation method is possible under an expanded set of causal assumptions relative to inference with well-defined interventions, which reflects the lower level of evidence that is expected with ill-defined interventions. Application to the self-harm example in the Victorian Adolescent Health Cohort Study illustrates the value of our proposal for informing the design and evaluation of actual interventions in the future.
许多流行病学问题都涉及到潜在的干预措施，以改变被认为介导关联的途径。例如，我们考虑一项研究，该研究调查了在年轻成年人中进行干预的益处，以改善青少年自残者相对于健康同龄人在中年期较差的心理社会结局。这带来了两个方法学挑战。首先，迄今为止，中介方法主要集中在发现途径这一难以捉摸的任务上，而不是评估中介干预措施。其次，此类问题的复杂性通常使得没有明确界定的中介干预措施（即实际治疗、计划等），对于相关人群、感兴趣的结果和时间段存在数据。相反，研究人员必须依赖于暴露（非干预）数据，即对于中介措施（例如抑郁症状）的测量，对于这些措施，实际可以实施的改变它们的干预措施尚不清楚。我们提出了一个新的框架，通过定义与针对多个中介的假设干预的目标试验相关的中介效应来解决这些挑战，对于这些中介，我们模拟了它们的效应。具体来说，我们指定了一个目标试验，该试验解决了三个与政策相关的问题，这些问题涉及到假设干预措施的影响，这些干预措施将改变中介的分布（在各种相互依存假设下分别、联合或顺序），以符合用户指定的、可以用观察数据模拟的分布。然后，我们定义了新的干预效应，这些效应与该试验相关联，通过将中介设定为从这些分布中随机抽取的随机数来模拟这些效应。我们表明，与具有明确干预措施的推理相比，在扩展的因果假设集下使用 g 计算方法进行估计是可能的，这反映了对于不明确干预措施的证据水平较低。对维多利亚青少年健康队列研究中自残示例的应用说明了我们的建议对于未来告知实际干预措施的设计和评估的价值。
