Rudolph Kara E, Sofrygin Oleg, Zheng Wenjing, van der Laan Mark J
Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA.
Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA.
Epidemiol Methods. 2018;7(1). doi: 10.1515/em-2017-0007. Epub 2017 Dec 13.
Causal mediation analysis can improve understanding of the mechanism s underlying epidemiologic associations. However, the utility of natural direct and indirect effect estimation has been limited by the assumption of no confounder of the mediator-outcome relationship that is affected by prior exposure (which we call an intermediate confounder)--an assumption frequently violated in practice.
We build on recent work that identified alternative estimands that do not require this assumption and propose a flexible and double robust targeted minimum loss-based estimator for stochastic direct and indirect effects. The proposed method intervenes stochastically on the mediator using a distribution which conditions on baseline covariates and marginalizes over the intermediate confounder.
We demonstrate the estimator's finite sample and robustness properties in a simple simulation study. We apply the method to an example from the Moving to Opportunity experiment. In this application, randomization to receive a housing voucher is the treatment/instrument that influenced moving with the voucher out of public housing, which is the intermediate confounder. We estimate the stochastic direct effect of randomization to the voucher group on adolescent marijuana use not mediated by change in school district and the stochastic indirect effect mediated by change in school district. We find no evidence of mediation.
Our estimator is easy to implement in standard statistical software, and we provide annotated R code to further lower implementation barriers.
因果中介分析有助于加深对流行病学关联潜在机制的理解。然而，自然直接效应和间接效应估计的效用受到一个假设的限制，即不存在受先前暴露影响的中介 - 结局关系的混杂因素（我们称之为中间混杂因素）——这一假设在实际中经常被违反。
我们基于最近的研究成果，该研究确定了无需此假设的替代估计量，并提出了一种灵活且具有双重稳健性的基于目标最小损失的随机直接和间接效应估计器。所提出的方法使用一种基于基线协变量的分布对中介进行随机干预，并对中间混杂因素进行边缘化处理。
我们在一个简单的模拟研究中展示了该估计器的有限样本和稳健性。我们将该方法应用于“搬到机会”实验中的一个例子。在这个应用中，随机分配获得住房券是影响使用住房券搬出公共住房的处理/工具，而这就是中间混杂因素。我们估计了随机分配到住房券组对未由学区变化介导的青少年大麻使用的随机直接效应，以及由学区变化介导的随机间接效应。我们没有发现中介作用的证据。
我们的估计器易于在标准统计软件中实现，并且我们提供了带注释的R代码以进一步降低实施障碍。
