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An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19
Liwen Su1, Jingyi Zhang1, Fangrong Yan1
1State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu Province, China.
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.
Insights
This study introduces an adaptive clinical trial design for COVID-19 drug discovery, improving efficiency and reducing sample size. The new method accelerates the identification of effective treatments while ensuring ethical considerations.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Pharmaceutical Sciences
Background:
- Thousands of COVID-19 clinical trials exist, but traditional designs are often inefficient.
- Pandemic diseases require novel trial designs due to timeliness, drug repurposing, and case spikes.
- Accelerating drug discovery for emerging infectious diseases is a critical unmet need.
Purpose of the Study:
- To develop an efficient and adaptive clinical trial platform for accelerated drug discovery.
- To enhance the screening of effective and optimal COVID-19 treatments.
- To address the limitations of traditional randomized controlled trials in pandemic settings.
Main Methods:
- Proposed an adaptive information borrowing platform for sequential drug candidate testing.
- Utilized power prior for information borrowing and time trend calibration for baseline drift.
- Implemented adaptive randomization for ethical considerations and faster trial completion.
Main Results:
- The adaptive design demonstrated excellent operating characteristics, controlling Type I error and increasing power.
- Early stopping rules were effectively triggered for both efficacy and futility.
- Information borrowing significantly improved the probability of screening promising drugs and reduced sample size.
- Time trend calibration proved robust across various baseline drifts.
Conclusions:
- The proposed design enhances efficiency, saves sample size, and meets ethical requirements for COVID-19 trials.
- This adaptive platform accelerates the screening of promising treatments and identification of optimal therapies.
- The design is well-suited for pandemic conditions, offering a more effective approach to drug development.
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