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US FDA’s definition of trial design “rigor” evolving, study says

Posted by on 11 April 2023
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US FDA expectations for “rigors trial design” are evolving to place more of an emphasis on patient centricity and use of advance analytics according to a new report.

The study - which was commissioned by Novotech – examined how the US Food and Drug Administrations (FDA’s) attitude to trials is changing with an eye on helping biotechs seeking to test products in the world’s largest drug market.

And the key finding is that while the Agency’s language around "rigorous trial design" has not changed, its definition of rigor is evolving rapidly in areas like patient-centricity and analytics.

“The FDA places greater emphasis on incorporating patient-centered outcomes into clinical trials, which involves assessing the impact of a treatment on patient-reported symptoms, quality of life, and other aspects that are important to patients.

“This shift has led to the development of new trial design approaches that focus on patient-reported outcomes, such as patient preference studies and patient-focused drug development,” the authors write.

Innovation

Evolving regulatory requirements have also impacted how the FDA defines rigor in trial design according to the authors.

They point to guidance on the use of biomarkers in clinical trials, as well as on the collection of safety data in early-phase clinical trials as examples.

The authors also suggested the FDA’s support for novel designs is informing its assessment of clinical trial applications.

“The FDA has encouraged the use of novel trial designs that can increase the efficiency and effectiveness of clinical trials,” they write, citing the increased popularity of adaptive designs at the height of the pandemic as an example.

However, while the FDA’s support for novel designs is an indication of its willingness to embrace innovation, from a rigor standpoint new ways of trialing medicines necessitate greater analytical expertise the authors write.

“These innovative trial designs present numerous statistical challenges, requiring solid expertise in order to be navigated.”

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