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Understanding and predicting COVID-19 clinical trial completion vs. cessation

As of March 30 2021, over 5,193 COVID-19 clinical trials have been registered through Clinicaltrial.gov. Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study). On the other hand, 909 trials have been completed (indicating the completion of the study)...

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Autores principales: Elkin, Magdalyn E., Zhu, Xingquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274906/
https://www.ncbi.nlm.nih.gov/pubmed/34252108
http://dx.doi.org/10.1371/journal.pone.0253789
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author Elkin, Magdalyn E.
Zhu, Xingquan
author_facet Elkin, Magdalyn E.
Zhu, Xingquan
author_sort Elkin, Magdalyn E.
collection PubMed
description As of March 30 2021, over 5,193 COVID-19 clinical trials have been registered through Clinicaltrial.gov. Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study). On the other hand, 909 trials have been completed (indicating the completion of the study). In this study, we propose to study underlying factors of COVID-19 trial completion vs. cessation, and design predictive models to accurately predict whether a COVID-19 trial may complete or cease in the future. We collect 4,441 COVID-19 trials from ClinicalTrial.gov to build a testbed, and design four types of features to characterize clinical trial administration, eligibility, study information, criteria, drug types, study keywords, as well as embedding features commonly used in the state-of-the-art machine learning. Our study shows that drug features and study keywords are most informative features, but all four types of features are essential for accurate trial prediction. By using predictive models, our approach achieves more than 0.87 AUC (Area Under the Curve) score and 0.81 balanced accuracy to correctly predict COVID-19 clinical trial completion vs. cessation. Our research shows that computational methods can deliver effective features to understand difference between completed vs. ceased COVID-19 trials. In addition, such models can also predict COVID-19 trial status with satisfactory accuracy, and help stakeholders better plan trials and minimize costs.
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spelling pubmed-82749062021-07-27 Understanding and predicting COVID-19 clinical trial completion vs. cessation Elkin, Magdalyn E. Zhu, Xingquan PLoS One Research Article As of March 30 2021, over 5,193 COVID-19 clinical trials have been registered through Clinicaltrial.gov. Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study). On the other hand, 909 trials have been completed (indicating the completion of the study). In this study, we propose to study underlying factors of COVID-19 trial completion vs. cessation, and design predictive models to accurately predict whether a COVID-19 trial may complete or cease in the future. We collect 4,441 COVID-19 trials from ClinicalTrial.gov to build a testbed, and design four types of features to characterize clinical trial administration, eligibility, study information, criteria, drug types, study keywords, as well as embedding features commonly used in the state-of-the-art machine learning. Our study shows that drug features and study keywords are most informative features, but all four types of features are essential for accurate trial prediction. By using predictive models, our approach achieves more than 0.87 AUC (Area Under the Curve) score and 0.81 balanced accuracy to correctly predict COVID-19 clinical trial completion vs. cessation. Our research shows that computational methods can deliver effective features to understand difference between completed vs. ceased COVID-19 trials. In addition, such models can also predict COVID-19 trial status with satisfactory accuracy, and help stakeholders better plan trials and minimize costs. Public Library of Science 2021-07-12 /pmc/articles/PMC8274906/ /pubmed/34252108 http://dx.doi.org/10.1371/journal.pone.0253789 Text en © 2021 Elkin, Zhu https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Elkin, Magdalyn E.
Zhu, Xingquan
Understanding and predicting COVID-19 clinical trial completion vs. cessation
title Understanding and predicting COVID-19 clinical trial completion vs. cessation
title_full Understanding and predicting COVID-19 clinical trial completion vs. cessation
title_fullStr Understanding and predicting COVID-19 clinical trial completion vs. cessation
title_full_unstemmed Understanding and predicting COVID-19 clinical trial completion vs. cessation
title_short Understanding and predicting COVID-19 clinical trial completion vs. cessation
title_sort understanding and predicting covid-19 clinical trial completion vs. cessation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274906/
https://www.ncbi.nlm.nih.gov/pubmed/34252108
http://dx.doi.org/10.1371/journal.pone.0253789
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