Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics During Epidemic Outbreaks
Author(s)Chaudhuri, Shomesh Ernesto; Lo, Andrew W; Xiao, Danying; Xu, Qingyang
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Sloan School of Management
Harvard Data Science Review
Chaudhuri, Shomesh et al. "Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics During Epidemic Outbreaks." Harvard Data Science Review (May 2020): doi.org/10.1162/99608f92.7656c213
Final published version