Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics During Epidemic Outbreaks

• Main Authors: Chaudhuri, Shomesh Ernesto (Author), Lo, Andrew W (Author), Xiao, Danying (Author), Xu, Qingyang (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Sloan School of Management (Contributor)
• Format: Article
• Language: English
• Published: MIT Press, 2021-02-17T22:28:00Z.
• Subjects: Article
• Online Access: Get fulltext

 In the midst of epidemics such as COVID-19, therapeutic candidates are unlikely to be able to complete the usual multiyear clinical trial and regulatory approval process within the course of an outbreak. We apply a Bayesian adaptive patient-centered model-which minimizes the expected harm of false positives and false negatives-to optimize the clinical trial development path during such outbreaks. When the epidemic is more infectious and fatal, the Bayesian-optimal sample size in the clinical trial is lower and the optimal statistical significance level is higher For COVID-19 (assuming a static and initial infection percentage of 0.1%), the optimal significance level is 7.1% for a clinical trial of a nonvaccine anti-infective therapeutic and 13.6% for that of a vaccine. For a dynamic decreasing from 3 to 1.5, the corresponding values are 14.4% and 26.4%, respectively. Our results illustrate the importance of adapting the clinical trial design and the regulatory approval process to the specific parameters and stage of the epidemic.