Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.
Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.
Biometrics. 2023 Dec;79(4):3126-3139. doi: 10.1111/biom.13850. Epub 2023 Apr 3.
Natural direct and indirect effects are mediational estimands that decompose the average treatment effect and describe how outcomes would be affected by contrasting levels of a treatment through changes induced in mediator values (in the case of the indirect effect) or not through induced changes in the mediator values (in the case of the direct effect). Natural direct and indirect effects are not generally point-identified in the presence of a treatment-induced confounder; however, they may be identified if one is willing to assume monotonicity between the treatment and the treatment-induced confounder. We argue that this assumption may be reasonable in the relatively common encouragement-design trial setting, where the intervention is randomized treatment assignment and the treatment-induced confounder is whether or not treatment was actually taken/adhered to. We develop efficiency theory for the natural direct and indirect effects under this monotonicity assumption, and use it to propose a nonparametric, multiply robust estimator. We demonstrate the finite sample properties of this estimator using a simulation study, and apply it to data from the Moving to Opportunity Study to estimate the natural direct and indirect effects of being randomly assigned to receive a Section 8 housing voucher-the most common form of federal housing assistance-on risk developing any mood or externalizing disorder among adolescent boys, possibly operating through various school and community characteristics.
自然直接和间接效应是中介效应估计量，它们分解了平均处理效应，并描述了通过改变中介值（在间接效应的情况下）或不通过改变中介值（在直接效应的情况下），对照治疗水平会如何影响结果。在存在治疗引起的混杂因素的情况下，自然直接和间接效应通常不是点识别的；然而，如果愿意假设治疗和治疗引起的混杂因素之间存在单调性，它们可能是可识别的。我们认为，在相对常见的鼓励设计试验设置中，这种假设可能是合理的，其中干预是随机治疗分配，而治疗引起的混杂因素是是否实际接受/遵守治疗。我们在这种单调性假设下为自然直接和间接效应开发了效率理论，并使用它提出了一种非参数、多重稳健估计量。我们使用模拟研究来展示该估计量的有限样本性质，并将其应用于来自“机遇转移研究”的数据，以估计随机分配接受第 8 节住房券的自然直接和间接效应——这是联邦住房援助的最常见形式——对青春期男孩发展任何情绪或外化障碍的风险的影响，可能通过各种学校和社区特征发挥作用。
