Oslo Center for Biostatistics and Epidemiology, Department for Biostatistics, IMB, University of Oslo, Oslo, Norway.
Department of Medicine, Diakonhjemmet Hospital, Oslo, Norway.
Biom J. 2020 May;62(3):532-549. doi: 10.1002/bimj.201800263. Epub 2019 Feb 19.
We discuss causal mediation analyses for survival data and propose a new approach based on the additive hazards model. The emphasis is on a dynamic point of view, that is, understanding how the direct and indirect effects develop over time. Hence, importantly, we allow for a time varying mediator. To define direct and indirect effects in such a longitudinal survival setting we take an interventional approach (Didelez, 2018) where treatment is separated into one aspect affecting the mediator and a different aspect affecting survival. In general, this leads to a version of the nonparametric g-formula (Robins, 1986). In the present paper, we demonstrate that combining the g-formula with the additive hazards model and a sequential linear model for the mediator process results in simple and interpretable expressions for direct and indirect effects in terms of relative survival as well as cumulative hazards. Our results generalize and formalize the method of dynamic path analysis (Fosen, Ferkingstad, Borgan, & Aalen, 2006; Strohmaier et al., 2015). An application to data from a clinical trial on blood pressure medication is given.
我们讨论了生存数据的因果中介分析，并提出了一种基于相加风险模型的新方法。重点是从动态的角度，即了解直接和间接效应如何随时间发展。因此，重要的是，我们允许中介是时变的。为了在这种纵向生存环境中定义直接和间接效应，我们采取一种干预方法（Didelez，2018），其中治疗分为影响中介的一个方面和影响生存的不同方面。一般来说，这导致了非参数 g 公式的一个版本（Robins，1986）。在本文中，我们证明了将 g 公式与相加风险模型以及中介过程的序贯线性模型相结合，可以得到直接和间接效应的简单和可解释的表达式，这些表达式涉及相对生存和累积风险。我们的结果概括和形式化了动态路径分析的方法（Fosen、Ferkingstad、Borgan 和 Aalen，2006；Strohmaier 等人，2015）。对血压药物临床试验数据的应用进行了说明。
