Halloran M Elizabeth, Hudgens Michael G
Center for Inference and Dynamics of Infectious Diseases, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center; Department of Biostatistics, School of Public Health, University of Washington.
Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill.
Curr Epidemiol Rep. 2016 Dec;3(4):297-305. doi: 10.1007/s40471-016-0086-4. Epub 2016 Jul 28.
One hundred years ago Sir Ronald Ross published his treatise on a general Theory of Happenings. Dependent happenings are those in which the frequency depends on the number already affected. When there is dependency of events, interventions can have different types of effects. Interventions such as vaccination can have direct protective effects for the person receiving the treatment, as well as indirect/spillover effects for others in the population. Causal inference is a framework for carefully defining the causal effect of a treatment, exposure, or policy, and then determining conditions under which such effects can be estimated from the observed data. We consider here scenarios in which the potential outcomes of an individual can depend on the treatment of other individuals in the population, known as causal inference with interference. Much of the research so far has assumed the population is divided into groups or clusters, and individuals can interfere with others within their clusters but not across clusters. Recent developments have assumed more general forms of interference. We review some of the different types of effects that have been defined for dependent happenings, particularly using the methods of causal inference with interference. Many of the methods are applicable across disciplines, such as infectious diseases, social sciences, and economics.
一百年前，罗纳德·罗斯爵士发表了他关于事件一般理论的论文。相依事件是指其发生频率取决于已受影响数量的事件。当事件存在相依性时，干预措施可能会产生不同类型的效果。诸如接种疫苗之类的干预措施，对接受治疗的人可产生直接保护作用，对人群中的其他人则可产生间接/溢出效应。因果推断是一个框架，用于仔细界定一项治疗、暴露因素或政策的因果效应，然后确定可根据观测数据估计此类效应的条件。我们在此考虑个体的潜在结果可能取决于人群中其他个体治疗情况的场景，即所谓的有干扰的因果推断。到目前为止，大部分研究都假定人群被划分为组或集群，个体可在其所在集群内对其他个体产生干扰，但不能跨集群产生干扰。最近的进展假定了更一般的干扰形式。我们回顾一些针对相依事件已定义的不同类型的效应，特别是使用有干扰的因果推断方法。许多方法适用于多个学科，如传染病学、社会科学和经济学。
