Zheng Wenjing, van der Laan Mark
Division of Biostatistics, University of California, Berkeley.
J Causal Inference. 2017 Sep;5(2). doi: 10.1515/jci-2016-0006. Epub 2017 Jun 23.
In this paper, we study the effect of a time-varying exposure mediated by a time-varying intermediate variable. We consider general longitudinal settings, including survival outcomes. At a given time point, the exposure and mediator of interest are influenced by past covariates, mediators and exposures, and affect future covariates, mediators and exposures. Right censoring, if present, occurs in response to past history. To address the challenges in mediation analysis that are unique to these settings, we propose a formulation in terms of random interventions based on conditional distributions for the mediator. This formulation, in particular, allows for well-defined natural direct and indirect effects in the survival setting, and natural decomposition of the standard total effect. Upon establishing identifiability and the corresponding statistical estimands, we derive the efficient influence curves and establish their robustness properties. Applying Targeted Maximum Likelihood Estimation, we use these efficient influence curves to construct multiply robust and efficient estimators. We also present an inverse probability weighted estimator and a nested non-targeted substitution estimator for these parameters.
在本文中，我们研究由时变中间变量介导的时变暴露的效应。我们考虑一般的纵向研究设置，包括生存结局。在给定的时间点，感兴趣的暴露和中间变量受到过去的协变量、中间变量和暴露的影响，并影响未来的协变量、中间变量和暴露。如果存在右删失，则其发生是对过去历史的响应。为应对这些设置中中介分析所特有的挑战，我们基于中间变量的条件分布，提出一种基于随机干预的公式化方法。特别是，这种公式化方法允许在生存设置中明确界定自然直接效应和间接效应，以及标准总效应的自然分解。在确立可识别性和相应的统计估计量后，我们推导有效影响曲线并确立其稳健性属性。应用靶向最大似然估计，我们使用这些有效影响曲线构建多重稳健且有效的估计量。我们还给出了这些参数的逆概率加权估计量和嵌套非靶向替代估计量。
