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 Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study).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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| Format: | Article |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0253789&type=printable |
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| author | Magdalyn E Elkin Xingquan Zhu |
| author_facet | Magdalyn E Elkin Xingquan Zhu |
| author_sort | Magdalyn E Elkin |
| collection | DOAJ |
| 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 Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study).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. |
| format | Article |
| id | doaj-art-4a8aa3099a524cc89d5cb3ec94cc500a |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-4a8aa3099a524cc89d5cb3ec94cc500a2025-08-20T02:17:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01167e025378910.1371/journal.pone.0253789Understanding and predicting COVID-19 clinical trial completion vs. cessation.Magdalyn E ElkinXingquan ZhuAs 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0253789&type=printable |
| spellingShingle | Magdalyn E Elkin Xingquan Zhu Understanding and predicting COVID-19 clinical trial completion vs. cessation. PLoS ONE |
| 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 |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0253789&type=printable |
| work_keys_str_mv | AT magdalyneelkin understandingandpredictingcovid19clinicaltrialcompletionvscessation AT xingquanzhu understandingandpredictingcovid19clinicaltrialcompletionvscessation |