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_39
Snippet: We first analyze the case when the infectivity remains constant in time (e.g. in the absence of effective NPIs). For the fixed-sample Bayesian RCT on a non-vaccine anti-infective therapeutic, as increases from 2 to 4 (Rows 1 to 2 of Table 3 ), the optimal sample size of each experimental arm decreases from 242 to 152 and the optimal Type I error rate drastically increases from 7.1% to 17.3% (Figure 1 ), much higher than the traditional 5% thresho.....
Document: We first analyze the case when the infectivity remains constant in time (e.g. in the absence of effective NPIs). For the fixed-sample Bayesian RCT on a non-vaccine anti-infective therapeutic, as increases from 2 to 4 (Rows 1 to 2 of Table 3 ), the optimal sample size of each experimental arm decreases from 242 to 152 and the optimal Type I error rate drastically increases from 7.1% to 17.3% (Figure 1 ), much higher than the traditional 5% threshold. As the epidemic spreads across the population more rapidly, the Bayesian RCT model has greater pressure to expedite the approval process and a much higher tolerance of false positive outcomes.
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