Fernanda Bravo | UCLA Anderson School of Management


Problem Definition: To approve a novel drug therapy, the U.S. Food and Drug Administration (FDA) requires clinical trial evidence demonstrating efficacy with 2.5% statistical significance, although the agency often uses regulatory discretion when interpreting these standards. Factors including disease severity, prevalence, and availability of existing therapies are qualitatively considered, yet current guidelines fail to systematically consider such characteristics in approval decisions.

Practical Relevance: New drug approval requires weighing the risks of committing type I and II errors against the potential benefits of introducing life-saving therapies. Approval standards tailored to individual diseases could improve treatment options for patients with few alternatives, potentially incentivizing pharmaceutical companies to invest in neglected diseases.

Methodology: We propose a novel queueing framework to analyze the FDA’s drug approval decision-making process that explicitly incorporates these factors, as well as obsolescence—when newer drugs replace older formulas—through the use of pre-emptive M/M/1/1 queues. Using public data encompassing all registered U.S. clinical trials and FDA-approved drugs, we estimate parameters for three high-burden diseases: breast cancer, HIV, and hypertension.

Results: Given an objective of maximizing net societal benefits, including health benefits and the monetary value of drug approval/rejection, the optimal policy relaxes approval standards for drugs targeting diseases with long clinical trials, high attrition during development, or low R&D intensity. Our results indicate that the current 2.5% significance level is too stringent for some diseases yet too lenient for others. A counterfactual analysis demonstrates that the FDA’s Fast Track program—offering expedited review of therapies for life-threatening diseases—achieves a level of societal benefit that cannot be attained by solely changing approval standards.

Managerial Implications: Our study offers a transparent, quantitative framework that can help the FDA issue disease-specific approval guidelines based on underlying disease severity, prevalence, and characteristics of the drug development process and existing market.