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

Qingyang Xu; Shomesh Chaudhuri; Danying Xiao; Andrew W Lo

We apply the Bayesian adaptive patient-centered model of to clinical trials for therapeutics that treat infectious diseases during an epidemic outbreak. Using a simple epidemiological model, we find that the optimal sample size in the clinical trial decreases with the infectivity of the epidemic, measured by the basic reproduction number . At the same time, the optimal Type I error rate increases with . Lower levels of initial infection increase the number of subjects required to verify the therapeutic efficacy of the therapeutic under investigation, while higher levels of mortality increase the optimal sample size. The results confirm our intuition that clinical trials should be expedited and a higher false positive rate should be tolerated when the epidemic spreads more rapidly through the population, has a higher mortality rate, and has already infected a sizable portion of the population at the beginning of the RCT.