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Can Bayesian Extrapolation Improve FDA Regulation of Off-Label Uses of Drugs and Devices?

FDLI's Food and Drug Policy Forum; 4(5), 1-12, 2014

16 Pages Posted: 5 Jun 2014  

Ryan Abbott

University of Surrey School of Law; University of California, Los Angeles - David Geffen School of Medicine

Ian Ayres

Yale University - Yale Law School; Yale University - Yale School of Management

Date Written: May 28, 2014

Abstract

A recurring issue for evidence-based regulation of medicine is deciding whether to extend governmental approval from an approved use with sufficient current evidence of safety and efficacy to a novel use where such evidence is currently lacking. This “extrapolation” problem can arise in several contexts: (i) diagnosis extrapolation occurs when physicians want to use an approved drug or device to treat a new condition; (ii) patient extrapolation occurs when physicians want to use an existing drug or device to treat a new population with a given condition; (iii) dosage extrapolation occurs when physicians want to use an existing drug or device for a new duration, schedule of use, or at a new dosage; (iv) treatment extrapolation occurs when physicians want to use a new drug or device that is related to an approved counterpart.

The logic of pre-approval testing, and the precautionary principal (first, do no harm), would seem to counsel prohibiting extrapolation approvals until after traditional safety and efficacy evidence exists. We reject that approach as overly conservative and instead propose a more dynamic and evolving evidence-based regime based on Bayes’ Law fundamentally, the science of learning. To apply Bayesian decision-making, one needs to (i) form a “prior” belief based on existing evidence, (ii) gather additional information, and (iii) update the prior belief. A system that allows interim periods of use can provide physicians and patients with greater treatment options while providing regulators with valuable evidence about the safety and efficacy of the proposed extrapolation. Indeed, off label drug use is legal and sometimes the medical standard of care. In contrast, a precautionary requirement conditioning all approvals on pre-existing evidence for uses that constitute just slight extrapolations along just one of these four dimensions sacrifices probable short-term health benefits at the alter of precaution. Harm is not only associated with permitting access to unsafe products but also with restricting access to beneficial products.

We call for policy changes in reporting, testing, and enforcement regulations to provide a more layered and dynamic system of regulatory incentives. Our proposals are Bayesian because they force policymakers to (i) assess and acknowledge the imperfect nature of their prior beliefs regarding off-label use, (ii) gather, when cost-effective, additional information, and (iii) take action in terms of approvals, reimbursements, and enforcement based on continual updating. We aim to put Bayesianism into regulatory practice.

Suggested Citation

Abbott, Ryan and Ayres, Ian, Can Bayesian Extrapolation Improve FDA Regulation of Off-Label Uses of Drugs and Devices? (May 28, 2014). FDLI's Food and Drug Policy Forum; 4(5), 1-12, 2014. Available at SSRN: https://ssrn.com/abstract=2445625

Ryan Benjamin Abbott (Contact Author)

University of Surrey School of Law ( email )

Guildford
Guildford, Surrey GU2 5XH
United Kingdom

University of California, Los Angeles - David Geffen School of Medicine ( email )

1000 Veteran Avenue, Box 956939
Los Angeles, CA 90095-6939
United States

Ian Ayres

Yale University - Yale Law School ( email )

P.O. Box 208215
New Haven, CT 06520-8215
United States
203-432-7101 (Phone)
203-432-2592 (Fax)

Yale University - Yale School of Management

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

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