Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics During Epidemic Outbreaks
Name
7656c213-07c0-42e1-b25f-2fe19a441c69.pdf
Description
Published version
Size
2.38 MB
Format
Adobe PDF
Checksum (MD5)
5fb0fa3630f6fb86ed308773f30d7444
Author(s)
Chaudhuri, Shomesh Ernesto
Lo, Andrew W
Xiao, Danying
Xu, Qingyang
Date Issued
May 2020
Journal
Harvard Data Science Review
Publisher
MIT Press
Citation
Chaudhuri, Shomesh et al. "Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics During Epidemic Outbreaks." Harvard Data Science Review (May 2020): doi.org/10.1162/99608f92.7656c213
Version
Final published version
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 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 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1162/99608f92.7656c213
