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dc.contributor.authorBerry, Donald A.
dc.contributor.authorBerry, Scott
dc.contributor.authorHale, Peter
dc.contributor.authorIsakov, Leah
dc.contributor.authorLo, Andrew W
dc.contributor.authorSiah, Kien Wei
dc.contributor.authorWong, Chi Heem
dc.date.accessioned2021-03-16T19:30:02Z
dc.date.available2021-03-16T19:30:02Z
dc.date.issued2020-12
dc.date.submitted2020-08
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/1721.1/130143
dc.description.abstractWe compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional and adaptive randomized clinical trials and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 756 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits-averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design-if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.en_US
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0244418en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleA cost/benefit analysis of clinical trial designs for COVID-19 vaccine candidatesen_US
dc.typeArticleen_US
dc.identifier.citationBerry, Donald A."A cost/benefit analysis of clinical trial designs for COVID-19 vaccine candidates." PLoS ONE 15, 12 (December 2020): e0244418. © 2020 Berry et al.en_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalPLoS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-03-10T18:33:29Z
dspace.orderedauthorsBerry, DA; Berry, S; Hale, P; Isakov, L; Lo, AW; Siah, KW; Wong, CHen_US
dspace.date.submission2021-03-10T18:33:30Z
mit.journal.volume15en_US
mit.journal.issue12en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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