Implementing less rigid, more flexible approaches to clinical trials can increase patient and site access to research, improve participant satisfaction, facilitate real-world data use, enroll more diverse patient populations, and enable more generalizable trial results. Ultimately, these flexible trial approaches help sponsors bring new therapies to patients faster. However, despite their numerous advantages, questions remain about how to maintain data quality when adopting these strategies. High-quality trial data must be fit-for-purpose, credible, and reliable to support regulatory decision-making without jeopardizing participant safety or the integrity of trial results.

To address these questions, CTTI has started a new project to gather diverse perspectives on the advantages and disadvantages of flexible trial approaches. This includes methods such as embedding trials in clinical practice, incorporating decentralized trial elements, and allowing for setting adjustments during trial participation. Through multi-partner roundtable discussions, in-depth interviews, and surveys, CTTI will identify critical concerns and considerations for maintaining data quality when using flexible trial approaches. CTTI will outline case examples of flexible approaches in trials, identify common themes and responses from partners around these case examples, and consider a toolbox that outlines the strengths of implementing flexible approaches and provides recommendations for how to maintain data quality when incorporating them.

Pamela Tenaerts, CTTI project team lead and representative from Medable, emphasized the potential of flexible methodologies, stating, “Embracing more flexible methodologies for data collection enables us to better represent the populations we’re looking to serve while providing a better experience for participants.”

CTTI’s work in this area will help to increase the use of flexible approaches in clinical trials, resulting in improved trial access, participant satisfaction, enrollment of diverse patient populations, and trial efficiency.