Jaki, Thomas and Barnett, Helen and Titman, Andrew and Mozgunov, Pavel (2024) A seamless Phase I/II platform design with a time-to-event efficacy endpoint for potential COVID-19 therapies. STATISTICAL METHODS IN MEDICAL RESEARCH, 33 (11-12). pp. 2115-2130. ISSN 0962-2802, 1477-0334
Full text not available from this repository. (Request a copy)Abstract
In the search for effective treatments for COVID-19, the initial emphasis has been on re-purposed treatments. To maximize the chances of finding successful treatments, novel treatments that have been developed for this disease in particular, are needed. In this article, we describe and evaluate the statistical design of the AGILE platform, an adaptive randomized seamless Phase I/II trial platform that seeks to quickly establish a safe range of doses and investigates treatments for potential efficacy. The bespoke Bayesian design (i) utilizes randomization during dose-finding, (ii) shares control arm information across the platform, and (iii) uses a time-to-event endpoint with a formal testing structure and error control for evaluation of potential efficacy. Both single-agent and combination treatments are considered. We find that the design can identify potential treatments that are safe and efficacious reliably with small to moderate sample sizes.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | TRIALS; BINARY; Adaptive platform trial; dose-escalation; COVID-19; randomized; seamless; time-to-improvement |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 300 Social sciences > 310 General statistics 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Informatics and Data Science > Department Machine Learning & Data Science > Lehrstuhl für Computational Statistics (Prof. Dr. Thomas Jaki) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 10 Dec 2025 07:34 |
| Last Modified: | 10 Dec 2025 07:38 |
| URI: | https://pred.uni-regensburg.de/id/eprint/63952 |
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