Is the Fda Too Conservative or Too Aggressive?: A Bayesian Decision Analysis of Clinical Trial Design

38 Pages Posted: 31 Aug 2015 Last revised: 29 Sep 2015

See all articles by Vahid Montazerhodjat

Vahid Montazerhodjat

Massachusetts Institute of Technology (MIT) - Sloan School of Management; Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Multiple version iconThere are 2 versions of this paper

Date Written: August 2015

Abstract

Implicit in the drug-approval process is a 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 relative costs 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 27.9%. However, 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%. 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.

Suggested Citation

Montazerhodjat, Vahid and Lo, Andrew W., Is the Fda Too Conservative or Too Aggressive?: A Bayesian Decision Analysis of Clinical Trial Design (August 2015). NBER Working Paper No. w21499. Available at SSRN: https://ssrn.com/abstract=2653510

Vahid Montazerhodjat (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

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Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science ( email )

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Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

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National Bureau of Economic Research (NBER) ( email )

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Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

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Cambridge, MA 02142
United States

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