Causal identification of infectious disease intervention effects in a clustered population
群组人群中传染病干预效应的因果识别AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究相互作用群组中的感染传播、治疗干扰及干预因果效应识别。
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群组人群中传染病干预效应的因果识别
- 研究问题
- 当感染会在人际间传播、个体干预也影响他人结局时，如何定义具有清晰含义的干预因果效应，并在群组内识别这些效应？
- 方法与思路
- 作者构建非参数结构因果目标量及识别框架，讨论感染时间与二元结局观测，并采用成对 Cox 型传播风险模型辅助有限样本推断。
- 材料陈述的贡献
- 摘要称框架推广已有目标量，覆盖随机与观察性研究，并通过多种疫苗试验设计的模拟比较已有及新提出的效应定义。
- 阅读关注点
- 应核对传播与干预干扰的区分、目标量的干预含义及识别条件，并检查仅有二元结局时的信息要求和传播模型设定。
- 阅读边界
- 所据为 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未列出具体识别假设，也未说明不同观测情形下的全部限制。
原文摘要
Causal identification of treatment effects for infectious disease outcomes in interconnected populations is challenging because infection outcomes may be transmissible to others, and treatment given to one individual may affect others' outcomes. Contagion, or transmissibility of outcomes, complicates standard conceptions of treatment interference in which an intervention delivered to one individual can affect outcomes of others. Several statistical frameworks have been proposed to measure causal treatment effects in this setting, including structural transmission models, mediation-based partnership models, and randomized trial designs. However, existing estimands for infectious disease intervention effects are of limited conceptual usefulness: Some are parameters in a structural model whose causal interpretation is unclear, others are causal effects defined only in a restricted two-person setting, and still others are nonparametric estimands that arise naturally in the context of a randomized trial but may not measure any biologically meaningful effect. In this paper, we describe a unifying formalism for defining nonparametric structural causal estimands and an identification strategy for learning about infectious disease intervention effects in clusters of interacting individuals when infection times are observed. The estimands generalize existing quantities and provide a framework for causal identification in randomized and observational studies, including situations where only binary infection outcomes are observed. A semiparametric class of pairwise Cox-type transmission hazard models is used to facilitate statistical inference in finite samples. A comprehensive simulation study compares existing and proposed estimands under a variety of randomized and observational vaccine trial designs.