An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19
Background. There have been thousands of clinical trials for COVID-19 to target effective treatments. However, quite a few of them are traditional randomized controlled trials with low efficiency. Considering the three particularities of pandemic disease: timeliness, repurposing, and case spike, new...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Canadian Journal of Infectious Diseases and Medical Microbiology |
| Online Access: | http://dx.doi.org/10.1155/2022/9293681 |
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| _version_ | 1849397030953156608 |
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| author | Liwen Su Jingyi Zhang Fangrong Yan |
| author_facet | Liwen Su Jingyi Zhang Fangrong Yan |
| author_sort | Liwen Su |
| collection | DOAJ |
| description | Background. There have been thousands of clinical trials for COVID-19 to target effective treatments. However, quite a few of them are traditional randomized controlled trials with low efficiency. Considering the three particularities of pandemic disease: timeliness, repurposing, and case spike, new trial designs need to be developed to accelerate drug discovery. Methods. We propose an adaptive information borrowing platform design that can sequentially test drug candidates under a unified framework with early efficacy/futility stopping. Power prior is used to borrow information from previous stages and the time trend calibration method deals with the baseline effectiveness drift. Two drug development strategies are applied: the comprehensive screening strategy and the optimal screening strategy. At the same time, we adopt adaptive randomization to set a higher allocation ratio to the experimental arms for ethical considerations, which can help more patients to receive the latest treatments and shorten the trial duration. Results. Simulation shows that in general, our method has great operating characteristics with type I error controlled and power increased, which can select effective/optimal drugs with a high probability. The early stopping rules can be successfully triggered to stop the trial when drugs are either truly effective or not optimal, and the time trend calibration performs consistently well with regard to different baseline drifts. Compared with the nonborrowing method, borrowing information in the design substantially improves the probability of screening promising drugs and saves the sample size. Sensitivity analysis shows that our design is robust to different design parameters. Conclusions. Our proposed design achieves the goal of gaining efficiency, saving sample size, meeting ethical requirements, and speeding up the trial process and is suitable and well performed for COVID-19 clinical trials to screen promising treatments or target optimal therapies. |
| format | Article |
| id | doaj-art-a9afe00a1f4d44a693f04d30cf136133 |
| institution | Kabale University |
| issn | 1918-1493 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Canadian Journal of Infectious Diseases and Medical Microbiology |
| spelling | doaj-art-a9afe00a1f4d44a693f04d30cf1361332025-08-20T03:39:10ZengWileyCanadian Journal of Infectious Diseases and Medical Microbiology1918-14932022-01-01202210.1155/2022/9293681An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19Liwen Su0Jingyi Zhang1Fangrong Yan2State Key Laboratory of Natural MedicinesState Key Laboratory of Natural MedicinesState Key Laboratory of Natural MedicinesBackground. There have been thousands of clinical trials for COVID-19 to target effective treatments. However, quite a few of them are traditional randomized controlled trials with low efficiency. Considering the three particularities of pandemic disease: timeliness, repurposing, and case spike, new trial designs need to be developed to accelerate drug discovery. Methods. We propose an adaptive information borrowing platform design that can sequentially test drug candidates under a unified framework with early efficacy/futility stopping. Power prior is used to borrow information from previous stages and the time trend calibration method deals with the baseline effectiveness drift. Two drug development strategies are applied: the comprehensive screening strategy and the optimal screening strategy. At the same time, we adopt adaptive randomization to set a higher allocation ratio to the experimental arms for ethical considerations, which can help more patients to receive the latest treatments and shorten the trial duration. Results. Simulation shows that in general, our method has great operating characteristics with type I error controlled and power increased, which can select effective/optimal drugs with a high probability. The early stopping rules can be successfully triggered to stop the trial when drugs are either truly effective or not optimal, and the time trend calibration performs consistently well with regard to different baseline drifts. Compared with the nonborrowing method, borrowing information in the design substantially improves the probability of screening promising drugs and saves the sample size. Sensitivity analysis shows that our design is robust to different design parameters. Conclusions. Our proposed design achieves the goal of gaining efficiency, saving sample size, meeting ethical requirements, and speeding up the trial process and is suitable and well performed for COVID-19 clinical trials to screen promising treatments or target optimal therapies.http://dx.doi.org/10.1155/2022/9293681 |
| spellingShingle | Liwen Su Jingyi Zhang Fangrong Yan An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19 Canadian Journal of Infectious Diseases and Medical Microbiology |
| title | An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19 |
| title_full | An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19 |
| title_fullStr | An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19 |
| title_full_unstemmed | An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19 |
| title_short | An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19 |
| title_sort | adaptive information borrowing platform design for testing drug candidates of covid 19 |
| url | http://dx.doi.org/10.1155/2022/9293681 |
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