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Is the FDA too conservative or too aggressive?: A Bayesian decision analysis of clinical trial design

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  • Isakov, Leah
  • Lo, Andrew W.
  • Montazerhodjat, Vahid

Abstract

Implicit in the drug-approval process is a host of decisions—target patient population, control group, primary endpoint, sample size, follow-up period, etc.—all of which determine the trade-off between Type I and Type II error. We explore the application of Bayesian decision analysis (BDA) to minimize the expected cost of drug approval, where the relative costs of the two types of errors are calibrated using U.S. Burden of Disease Study 2010 data. The results for conventional fixed-sample randomized clinical-trial designs suggest that for terminal illnesses with no existing therapies such as pancreatic cancer, the standard threshold of 2.5% is substantially more conservative than the BDA-optimal threshold of 23.9% to 27.8%. For relatively less deadly conditions such as prostate cancer, 2.5% is more risk-tolerant or aggressive than the BDA-optimal threshold of 1.2% to 1.5%. We compute BDA-optimal sizes for 25 of the most lethal diseases and show how a BDA-informed approval process can incorporate all stakeholders’ views in a systematic, transparent, internally consistent, and repeatable manner.

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  • 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.
    • Handle: RePEc:eee:econom:v:211:y:2019:i:1:p:117-136
    DOI: 10.1016/j.jeconom.2018.12.009
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    1. Yi Cheng, 2003. "Choosing sample size for a clinical trial using decision analysis," Biometrika, Biometrika Trust, vol. 90(4), pages 923-936, December.
    2. David J. Spiegelhalter & Laurence S. Freedman & Mahesh K. B. Parmar, 1994. "Bayesian Approaches to Randomized Trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 357-387, May.
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    Cited by:

    1. Donald A. Berry & Scott Berry & Peter Hale & Leah Isakov & Andrew W. Lo & Kien Wei Siah & Chi Heem Wong, 2020. "A Cost/Benefit Analysis of Clinical Trial Designs for COVID-19 Vaccine Candidates," NBER Working Papers 27882, National Bureau of Economic Research, Inc.
    2. Raymond J. March, 2021. "The FDA and the COVID‐19: A political economy perspective," Southern Economic Journal, John Wiley & Sons, vol. 87(4), pages 1210-1228, April.
    3. Casey B. Mulligan, 2021. "Peltzman Revisited: Quantifying 21st Century Opportunity Costs of FDA Regulation," NBER Working Papers 29574, National Bureau of Economic Research, Inc.
    4. 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.
    5. David J. Hebert & Michael D. Curry, 2022. "Optimal lockdowns," Public Choice, Springer, vol. 193(3), pages 263-274, December.
    6. Erin R. Lipman & John Deke & Mariel M. Finucane, 2022. "Bayesian Interpretation Of Cluster‐Robust Subgroup Impact Estimates: The Best Of Both Worlds," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(4), pages 1204-1224, September.
    7. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    8. Clancy, Matthew S. & Sneeringer, Stacy E., 2018. "How Much Does it Cost to Induce R&D in Animal Health?," 2018 Annual Meeting, August 5-7, Washington, D.C. 273865, Agricultural and Applied Economics Association.
    9. Steven Glazerman & Ira Nichols-Barrer & Jon Valant & Alyson Burnett, "undated". "Presenting School Choice Information to Parents: An Evidence-Based Guide, Appendix," Mathematica Policy Research Reports d418c5d8768d4ed8ade319330, Mathematica Policy Research.
    10. Thijssen, Jacco J.J. & Bregantini, Daniele, 2017. "Costly sequential experimentation and project valuation with an application to health technology assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 202-229.
    11. Stacy Sneeringer & Matt Clancy, 2020. "Incentivizing New Veterinary Pharmaceutical Products to Combat Antibiotic Resistance," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(4), pages 653-673, December.

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    More about this item

    Keywords

    Clinical trial design; Drug-approval process; FDA; Bayesian decision analysis; Adaptive design;
    All these keywords.

    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
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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