Network methods and design of randomized trials: Application to investigation of COVID-19 vaccination boosters
Year: 2022
Type: article
Abstract: 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.
Source: Clinical Trials
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: Fred Hutchinson Cancer Research Center, National Institutes of Health, Center for AIDS Research, University of Washington, University of California, San Diego, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Center for AIDS Research, University of California, San Diego
Awards: G2034299771, G342295615, Network modeling and robust estimation of the intraclass correlation coefficient to inform the design and analysis of cluster randomized trials for infectious diseases, G3789320706, Primary Infection Resource Consortium (PIRC), G495691247, G5187911018, Methods to Advance the HIV Prevention Research Agenda, G6122456536, G6382101173, G6582859664, Optimizing HIV prevention portfolios targeting people who inject drugs using dynamic economic modeling, G7103799864, G8259150102, Modeling and simulation tools for optimizing design of network-informed clinical trials of combination HIV prevention interventions, G9000124410, San Diego Center for AIDS Research (SD CFAR), Bridging Network Science and Causal Inference to Improve Public Health Response to Outbreaks and Endemic Diseases
OpenAlex ID: W4288068585