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

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

Page 7 of 27 All Rights Reserved One interesting feature of the Bayesian decision model is that the optimal Type I error rate is not a monotonic function , but rather has a global minimum of 8% at 1.7 for COVID-19, as shown in Figure 1 . As decreases below 1.7, the optimal Type I error rate increases. The intuition for this result that we define the loss of Type I error as the excess risk of being susceptible to infection , where is the fraction of the population that remains uninfected throughout the epidemic outbreak. When is small, is close to 100% and the excess risk is small compared to the benefit of preventing future deaths. Therefore, when the epidemic is not very infectious, the Bayesian decision model expedites the approval decision. This also confirms the intuition that smaller sample sizes are required in adaptive trials for diseases that affect a small fraction of the population. If we instead define the loss of Type I error as the absolute risk of being susceptible , we find that the optimal Type I error indeed monotonically increases with , as shown in Figure 11 of Supplementary Materials.