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

Shomesh Chaudhuri, Andrew Lo (), Danying Xiao and Qingyang Xu

No 27175, NBER Working Papers from National Bureau of Economic Research, Inc

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 R ₀ – 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 R ₀ 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.

JEL-codes: C11 C12 C44 C54 C9 C93 H12 H51 I1 I11 I12 I15 I18 (search for similar items in EconPapers)
Date: 2020-05
New Economics Papers: this item is included in nep-hea
Note: AP EH
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Citations: View citations in EconPapers (1)

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