49 Pages Posted: 11 Aug 2015 Last revised: 26 Jan 2016
Date Written: August 19, 2015
Implicit in the drug-approval process is a host of decisions -- target patient population, control group, primary endpoint, sample size, follow-up period -- 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.
Keywords: Clinical Trial Design; Drug-Approval Process; FDA; Bayesian Decision Analysis; Adaptive Design
JEL Classification: I18,C12, C44, C11
Suggested Citation: Suggested Citation
Isakov, Leah and Lo, Andrew W. and Montazerhodjat, Vahid, Is the FDA Too Conservative or Too Aggressive?: A Bayesian Decision Analysis of Clinical Trial Design (August 19, 2015). Available at SSRN: https://ssrn.com/abstract=2641547 or http://dx.doi.org/10.2139/ssrn.2641547