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Network methods and design of randomized trials: Application to investigation of COVID-19 vaccination boosters.
DeGruttola, Victor; Goyal, Ravi; Martin, Natasha K; Wang, Rui.
  • DeGruttola V; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Goyal R; Division of Infectious Diseases & Global Public Health, Department of Medicine, University of California San Diego La Jolla, CA, USA.
  • Martin NK; Division of Infectious Diseases & Global Public Health, Department of Medicine, University of California San Diego La Jolla, CA, USA.
  • Wang R; Division of Infectious Diseases & Global Public Health, Department of Medicine, University of California San Diego La Jolla, CA, USA.
Clin Trials ; 19(4): 363-374, 2022 08.
Article in English | MEDLINE | ID: covidwho-1957006
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: HIV Infections / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Vaccines / Variants Limits: Humans Language: English Journal: Clin Trials Journal subject: Medicine / Therapeutics Year: 2022 Document Type: Article Affiliation country: 17407745221111818

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Full text: Available Collection: International databases Database: MEDLINE Main subject: HIV Infections / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Vaccines / Variants Limits: Humans Language: English Journal: Clin Trials Journal subject: Medicine / Therapeutics Year: 2022 Document Type: Article Affiliation country: 17407745221111818