Working Paper: NBER ID: w21499
Authors: Vahid Montazerhodjat; Andrew W. Lo
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.
Keywords: No keywords provided
JEL Codes: C11; C12; C44; I10; I12; I13; I18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| FDA's conservative approach (D18) | hinder access to potentially life-saving therapies (I14) |
| standard threshold of 25% for type I error (C46) | overly conservative for terminal illnesses (I19) |
| BDA-optimal threshold of 27.9% (C24) | FDA's conservative approach for terminal illnesses (I19) |
| standard threshold of 25% for less severe conditions (C24) | more aggressive than BDA-optimal threshold of 12% (C70) |
| BDA-informed approval process (G28) | enhance ethical considerations in clinical trial designs (C90) |
| BDA framework (O19) | provide a more rational basis for decision-making (D91) |