Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Division of Infectious Diseases & Global Public Health, Department of Medicine, University of California San Diego La Jolla, CA, USA.
Clin Trials. 2022 Aug;19(4):363-374. doi: 10.1177/17407745221111818. Epub 2022 Jul 27.
Network science methods can be useful in design, monitoring, and analysis of randomized trials for control of spread of infections. Their usefulness arises from the role of statistical network models in molecular epidemiology and in study design. Computational models, such as agent-based models that propagate disease on simulated contact networks, can be used to investigate the properties of different study designs and analysis plans. Particularly valuable is the use of these methods to assess how magnitude and detectability of intervention effects depend on both individual-level and network-level characteristics of the enrolled populations. Such investigation also provides an important approach to assessing consequences of study data being incomplete or measured with error. To address these goals, we consider two statistical network models: exponential random graph models and the more flexible congruence class models. We focus first on an historical use of these methods in design and monitoring of a cluster randomized trial in Botswana to evaluate the effect of combination HIV prevention modalities compared to standard of care on HIV incidence. We then present a framework for the design of a study of booster vaccine effects on infection with, and forward transmission of, SARS-CoV-2 variants. Motivation for the study is driven in part by guidance from the United Kingdom to base approval of booster vaccines with "strain changes" that target variants on results of neutralizing antibody tests and information about safety, but without requiring evidence of clinical efficacy. Using designs informed by our agent-based network models, we show it may be feasible to conduct a trial of novel SARS-CoV-2 vaccines in a single large campus to obtain useful information regarding vaccine efficacy against susceptibility and infectiousness. If needed, the sample size could be increased by extending the study to a small number of campuses. Novel network methods may be useful in developing pragmatic SARS-CoV-2 vaccine trials that can leverage existing infrastructure to reduce costs and hasten the development of results.
网络科学方法可用于设计、监测和分析用于控制传染病传播的随机试验。它们的用途源于统计网络模型在分子流行病学和研究设计中的作用。计算模型，例如在模拟接触网络上传播疾病的基于代理的模型，可以用于研究不同研究设计和分析计划的特性。特别有价值的是使用这些方法来评估干预效果的大小和可检测性如何取决于所纳入人群的个体水平和网络水平特征。这种研究还为评估研究数据不完整或测量有误差的后果提供了一个重要方法。为了实现这些目标，我们考虑了两种统计网络模型：指数随机图模型和更灵活的同余类模型。我们首先关注这些方法在博茨瓦纳一项集群随机试验的设计和监测中的历史应用，该试验旨在评估与标准护理相比，联合 HIV 预防方式对 HIV 发病率的影响。然后，我们提出了一种设计研究增强疫苗对 SARS-CoV-2 变体感染和传播影响的框架。该研究的动机部分源于英国的指导意见，即根据中和抗体测试结果和安全性信息，批准针对变体的增强疫苗，而无需临床疗效证据。我们使用基于我们的基于代理的网络模型设计的研究表明，在一个大校园中进行新型 SARS-CoV-2 疫苗试验以获得有关疫苗对易感性和传染性的疗效的有用信息可能是可行的。如果需要，可以通过将研究扩展到少数几个校园来增加样本量。新型网络方法可能有助于开发实用的 SARS-CoV-2 疫苗试验，利用现有基础设施降低成本并加快结果的开发。
