Cai Xiaoxuan, Loh Wen Wei, Crawford Forrest W
Department of Biostatistics, Yale School of Public Health.
Department of Data Analysis, University of Ghent.
J Causal Inference. 2021 Jan;9(1):9-38. doi: 10.1515/jci-2019-0033. Epub 2021 Apr 5.
Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment - such as a vaccine - given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural assumptions that preclude realistic infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial.
定义和识别可传播传染病结果的因果干预效应具有挑战性，因为给予一个人的治疗（如疫苗）可能会影响其他人的感染结果。流行病学家提出了因果估计量，以使用两人伙伴关系模型来量化在传染情况下干预措施的效果。这些简单的概念模型帮助研究人员开发了与疫苗效果临床评估相关的因果估计量。然而，许多这些伙伴关系模型是在结构假设下制定的，这些假设排除了现实的传染病传播动态，限制了它们在实证干预试验中定义和识别因果治疗效果的概念实用性。在本文中，我们提出了在任意传染病传播动态下两人伙伴关系中的因果干预效应，并给出了非参数识别结果，展示了如何在实证试验中使用感染时间或二元结果数据来估计效应。关键的见解是，传染是一种因果现象，它会在感染结果上诱导条件独立性，可利用这些独立性来识别具有临床意义的因果估计量。将这些新的估计量与现有数量进行了比较，并使用HIV疫苗试验的实际模拟来说明结果。
