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Using simulation to aid trial design: Ring-vaccination trials
BACKGROUND: The 2014–6 West African Ebola epidemic highlights the need for rigorous, rapid clinical trial methods for vaccines. A challenge for trial design is making sample size calculations based on incidence within the trial, total vaccine effect, and intracluster correlation, when these paramete...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5378415/ https://www.ncbi.nlm.nih.gov/pubmed/28328984 http://dx.doi.org/10.1371/journal.pntd.0005470 |
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author | Hitchings, Matt David Thomas Grais, Rebecca Freeman Lipsitch, Marc |
author_facet | Hitchings, Matt David Thomas Grais, Rebecca Freeman Lipsitch, Marc |
author_sort | Hitchings, Matt David Thomas |
collection | PubMed |
description | BACKGROUND: The 2014–6 West African Ebola epidemic highlights the need for rigorous, rapid clinical trial methods for vaccines. A challenge for trial design is making sample size calculations based on incidence within the trial, total vaccine effect, and intracluster correlation, when these parameters are uncertain in the presence of indirect effects of vaccination. METHODS AND FINDINGS: We present a stochastic, compartmental model for a ring vaccination trial. After identification of an index case, a ring of contacts is recruited and either vaccinated immediately or after 21 days. The primary outcome of the trial is total vaccine effect, counting cases only from a pre-specified window in which the immediate arm is assumed to be fully protected and the delayed arm is not protected. Simulation results are used to calculate necessary sample size and estimated vaccine effect. Under baseline assumptions about vaccine properties, monthly incidence in unvaccinated rings and trial design, a standard sample-size calculation neglecting dynamic effects estimated that 7,100 participants would be needed to achieve 80% power to detect a difference in attack rate between arms, while incorporating dynamic considerations in the model increased the estimate to 8,900. This approach replaces assumptions about parameters at the ring level with assumptions about disease dynamics and vaccine characteristics at the individual level, so within this framework we were able to describe the sensitivity of the trial power and estimated effect to various parameters. We found that both of these quantities are sensitive to properties of the vaccine, to setting-specific parameters over which investigators have little control, and to parameters that are determined by the study design. CONCLUSIONS: Incorporating simulation into the trial design process can improve robustness of sample size calculations. For this specific trial design, vaccine effectiveness depends on properties of the ring vaccination design and on the measurement window, as well as the epidemiologic setting. |
format | Online Article Text |
id | pubmed-5378415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-53784152017-04-06 Using simulation to aid trial design: Ring-vaccination trials Hitchings, Matt David Thomas Grais, Rebecca Freeman Lipsitch, Marc PLoS Negl Trop Dis Research Article BACKGROUND: The 2014–6 West African Ebola epidemic highlights the need for rigorous, rapid clinical trial methods for vaccines. A challenge for trial design is making sample size calculations based on incidence within the trial, total vaccine effect, and intracluster correlation, when these parameters are uncertain in the presence of indirect effects of vaccination. METHODS AND FINDINGS: We present a stochastic, compartmental model for a ring vaccination trial. After identification of an index case, a ring of contacts is recruited and either vaccinated immediately or after 21 days. The primary outcome of the trial is total vaccine effect, counting cases only from a pre-specified window in which the immediate arm is assumed to be fully protected and the delayed arm is not protected. Simulation results are used to calculate necessary sample size and estimated vaccine effect. Under baseline assumptions about vaccine properties, monthly incidence in unvaccinated rings and trial design, a standard sample-size calculation neglecting dynamic effects estimated that 7,100 participants would be needed to achieve 80% power to detect a difference in attack rate between arms, while incorporating dynamic considerations in the model increased the estimate to 8,900. This approach replaces assumptions about parameters at the ring level with assumptions about disease dynamics and vaccine characteristics at the individual level, so within this framework we were able to describe the sensitivity of the trial power and estimated effect to various parameters. We found that both of these quantities are sensitive to properties of the vaccine, to setting-specific parameters over which investigators have little control, and to parameters that are determined by the study design. CONCLUSIONS: Incorporating simulation into the trial design process can improve robustness of sample size calculations. For this specific trial design, vaccine effectiveness depends on properties of the ring vaccination design and on the measurement window, as well as the epidemiologic setting. Public Library of Science 2017-03-22 /pmc/articles/PMC5378415/ /pubmed/28328984 http://dx.doi.org/10.1371/journal.pntd.0005470 Text en © 2017 Hitchings et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hitchings, Matt David Thomas Grais, Rebecca Freeman Lipsitch, Marc Using simulation to aid trial design: Ring-vaccination trials |
title | Using simulation to aid trial design: Ring-vaccination trials |
title_full | Using simulation to aid trial design: Ring-vaccination trials |
title_fullStr | Using simulation to aid trial design: Ring-vaccination trials |
title_full_unstemmed | Using simulation to aid trial design: Ring-vaccination trials |
title_short | Using simulation to aid trial design: Ring-vaccination trials |
title_sort | using simulation to aid trial design: ring-vaccination trials |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5378415/ https://www.ncbi.nlm.nih.gov/pubmed/28328984 http://dx.doi.org/10.1371/journal.pntd.0005470 |
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