dc.contributor.author | Berry, Donald A. | |
dc.contributor.author | Berry, Scott | |
dc.contributor.author | Hale, Peter | |
dc.contributor.author | Isakov, Leah | |
dc.contributor.author | Lo, Andrew W | |
dc.contributor.author | Siah, Kien Wei | |
dc.contributor.author | Wong, Chi Heem | |
dc.date.accessioned | 2021-03-16T19:30:02Z | |
dc.date.available | 2021-03-16T19:30:02Z | |
dc.date.issued | 2020-12 | |
dc.date.submitted | 2020-08 | |
dc.identifier.issn | 1932-6203 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/130143 | |
dc.description.abstract | We 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.iso | en | |
dc.publisher | Public Library of Science (PLoS) | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1371/journal.pone.0244418 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | PLoS | en_US |
dc.title | A cost/benefit analysis of clinical trial designs for COVID-19 vaccine candidates | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Berry, 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.department | Sloan School of Management | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.relation.journal | PLoS ONE | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2021-03-10T18:33:29Z | |
dspace.orderedauthors | Berry, DA; Berry, S; Hale, P; Isakov, L; Lo, AW; Siah, KW; Wong, CH | en_US |
dspace.date.submission | 2021-03-10T18:33:30Z | |
mit.journal.volume | 15 | en_US |
mit.journal.issue | 12 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Complete | |