FDA’s New Default: One Pivotal Trial for Drug Approval


February 26, 2026

A Paradigm Shift Sparking Optimism and Questions

In February 2026, the U.S. Food and Drug Administration (FDA) announced a landmark policy change that one adequate and well‑controlled pivotal trial, supplemented by confirmatory evidence, will now serve as the default basis for drug approval. This decisive shift — articulated by FDA Commissioner Marty Makary and CBER director Vinay Prasad in The New England Journal of Medicine — effectively ends a decades‑long “two‑trial dogma” and reframes the evidentiary foundation of U.S. drug regulation.

“Going forward, the FDA’s default position is that one adequate and well-controlled study, combined with confirmatory evidence, will serve as the basis of marketing authorization of novel products. The FDA will carefully examine all aspects of study design with particular focus on controls, end points, effect size, and statistical protocols.”1

It is important to remember that it has always been possible to obtain a marketing authorization on the basis of a single adequate and well-controlled study in combination with confirmative evidence, but typically this approach was mainly applied in breakthrough program designation, accelerated approval, and priority review pathways.

 

Why the FDA is moving away from two trials

Makary and Prasad argue that requiring two trials made sense when biology was poorly understood and therapeutics were often blunt instruments rather than targeted molecular tools. In today’s world, duplicative trials may be unnecessarily costly, slow, and redundant.

The original argument for two clinical trials is a statistical one: If a substance does not have any efficacy, then the chances of showing an effect in two studies are much lower than showing it in only one study.  The article quantifies this chance as 0.06% instead of 2.5%, assuming that the test is performed at the typically applied one-sided 2.5% level (but that calculation assumes that the two studies are independent of each other, which is not necessarily the case).

The more important argument is that modern drug development provides much more clarity on a precise mechanism of action, assessed by biomarkers as well as a variety of endpoints, thus supporting statistical with biologic inference.

They emphasize several points:

  • Modern science provides multiple layers of corroboration

Mechanistic data, class‑effect consistency, real‑world evidence, and surrogate endpoints can complement a single pivotal study.

  • Two trials don’t guarantee correctness

Even under the two‑trial regime, the FDA has approved drugs later found ineffective or unsafe — not because of too few trials, but because trial design quality matters more than quantity.

  • Lowering trial count may reduce costs and time

One pivotal trial can cost $30–150M and takes years to complete. Reducing this burden may spur innovation and could reduce price‑justification arguments tied to Research & Development investment.

 

Focus on trial design and analysis

The article clearly articulates the importance of various aspects of trial design to support the credibility of trial results, including the use of a contemporary control group, pre-specification of a hypothesis, choice of a primary endpoint, statistical power, randomization, and blinding.  These are key statistical aspects documented in the ICH E9 guidance on Statistical Principles for Clinical Trials, and as such they have been underlying drug development for almost 30 years. What is new and encouraging is that the article specifically states that these can also be provided by a Bayesian framework, referencing the recently published draft FDA guidance on this topic, and described by Cytel’s Savina Jaeger..

 

Unclear implications for global drug development

For most companies, drug development is a global business, and as such it’s unclear whether this change in FDA policy will affect the expectations from regulatory authorities in other regions and countries. Will they follow suit or maintain their current expectations? Cytel’s Strategic Consulting group will be monitoring this closely as this will have a fundamental impact on designing trials for global approvals.

It is also uncommon for the FDA to announce a major change in policy through a publication, so we will also monitor FDA’s official channels for further announcements on this topic in the future.

 

Final takeaway: A defining regulatory moment

The FDA’s new one‑trial default represents a significant policy shift in U.S. drug regulation. It aligns with trends in precision medicine, leverages mechanistic and statistical advances, and may unlock faster access and lower development burdens. Yet it also raises profound questions about evidence standards, risk tolerance, and the balance between speed and certainty. Most importantly, though, it reinforces the importance of solid statistical principles underlying credible drug development, with a clear statement that both Bayesian and frequentist approaches can provide them.

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Jürgen Hummel

Vice President, Innovative Statistics

Jürgen Hummel has been working in Biostatistics in the CRO, pharmaceutical, and health care industry since 1995. He has held various project-related, technical, and managerial positions with increasing seniority over the years, and he has experience in a wide range of therapeutic areas, particularly in early and late-stage oncology trials. Over the last 9 years, he headed up the Statistical Methodology groups at PPD and most recently at Novo Nordisk. In his role at Cytel, he provides statistical consultancy, which was an important part of his role in previous jobs as well.

Jürgen has shaped cross-industry collaborations for many years: he led the PSI/EFSPI Regulatory Special Interest Group for the last 5 years, is a member of the EFSPI Statistical Methods Leaders Group, and previously served on the PSI Board of Directors. He is passionate about adaptive designs and was EFPIA Deputy Topic Lead on the ICH E20 Expert Working Group on adaptive design for clinical trials until recently. He earned the German equivalent of an MSc in mathematics and economics at Augsburg University, and he is a Chartered Statistician with the Royal Statistical Society.

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