Working Paper: NBER ID: w27175
Authors: Shomesh Chaudhuri; Andrew W. Lo; Danying Xiao; Qingyang Xu
Abstract: 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 R₀ – 2 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 R₀ 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.
Keywords: Bayesian adaptive trials; anti-infective therapeutics; epidemic outbreaks; COVID-19; clinical trial optimization
JEL Codes: C11; C12; C44; C54; C9; C93; H12; H51; I1; I11; I12; I15; I18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Epidemic infectivity (I12) | Optimal sample size (C83) |
| Epidemic infectivity (I12) | Type I error rate (C52) |
| Bayesian adaptive clinical trial design (C11) | Optimal sample size (C83) |
| Epidemic severity (I12) | Regulatory threshold for approval (L51) |
| Epidemic infectivity (I12) | Urgency to approve therapeutics (I19) |
| Epidemic parameters (C22) | Trial outcomes (K41) |