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

Author

Listed:
  • 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 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.

Suggested Citation

  • Shomesh Chaudhuri & Andrew W. Lo & Danying Xiao & Qingyang Xu, 2020. "Bayesian Adaptive Clinical Trials for Anti‐Infective Therapeutics during Epidemic Outbreaks," NBER Working Papers 27175, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27175
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    References listed on IDEAS

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    1. Isakov, Leah & Lo, Andrew W. & Montazerhodjat, Vahid, 2019. "Is the FDA too conservative or too aggressive?: A Bayesian decision analysis of clinical trial design," Journal of Econometrics, Elsevier, vol. 211(1), pages 117-136.
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    Cited by:

    1. Shomesh E. Chaudhuri & Phillip Adamson & Dean Bruhn-Ding & Zied Ben Chaouch & David Gebben & Liliana Rincon-Gonzalez & Barry Liden & Shelby D. Reed & Anindita Saha & Daniel Schaber & Kenneth Stein & M, 2023. "Patient-Centered Clinical Trial Design for Heart Failure Devices via Bayesian Decision Analysis," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 16(4), pages 359-369, July.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I1 - Health, Education, and Welfare - - Health
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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