Author: Qingyang Xu; Shomesh Chaudhuri; Danying Xiao; Andrew W Lo
Title: Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics during Epidemic Outbreaks Document date: 2020_4_14
ID: 20hk99h4_8
Snippet: In this article, we apply the Bayesian adaptive protocol to anti-infective therapeutic development using a loss function that evolves over the course of an epidemic outbreak. We achieve an optimal balance between Type I and Type II errors for therapeutics that treat infectious diseases and identify the optimal time to reach the approval decision based on the accumulation of clinical evidence. Our results show that when the epidemic is more infect.....
Document: In this article, we apply the Bayesian adaptive protocol to anti-infective therapeutic development using a loss function that evolves over the course of an epidemic outbreak. We achieve an optimal balance between Type I and Type II errors for therapeutics that treat infectious diseases and identify the optimal time to reach the approval decision based on the accumulation of clinical evidence. Our results show that when the epidemic is more infectious, the necessary sample size of the RCT decreases, while the tolerable Type I error increases. This confirms our earlier intuition that potentially effective therapies that are known to be safe should receive expedited approval when an epidemic is spreading rapidly.
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