Fighting Time in Adaptive Trials

In this episode of "In the Interim…", Dr. Scott Berry explores the challenge of protracted endpoint timelines in adaptive clinical trials and the statistical strategies used to increase the rate of actionable information gain. Drawing on detailed case studies from breast cancer (I-SPY 2), Alzheimer’s disease (BAN 2401), diabetes (AWARD-5/Trulicity), and cardiac arrest, Scott addresses the technical demands of longitudinal modeling and interim data imputation for accelerating learning. The discussion prioritizes a critical, empirical perspective of demonstrating how carefully constructed statistical models, simulation, and Bayesian methods can convert interim patient data into more robust estimates of delayed outcomes and support key design adaptations. The episode is a direct account of the methods, uncertainties, and real-world impact of fighting time in adaptive trials.

Key Highlights

• Analyzes how delayed primary endpoints challenge adaptive trial efficiency, and how adaptive trial designs use accumulating in-trial data to inform adaptive allocation, arm graduation, and early trial conclusions.
• Dissects the use of longitudinal models in I-SPY 2, in which interim MRI measurements at one and three months are mapped to predicted six-month pathologic complete response, through an ordinal stratified, pre-specified modeling approach—illustrating both the strengths and limits of interim forecasting.
• Reviews the BAN 2401 adaptive Alzheimer’s trial, where early cognitive assessments were modeled to forecast 12-month outcomes enabling response adaptive randomization and sample size adaptation based on projections from interim data.
• Details the AWARD-5 seamless trial for dulaglutide (Trulicity), where strategic enrollment pacing, predictive modeling of early HbA1c and weight loss, and a utility function across four endpoints supported both dose selection and seamless transition to phase 3 without requiring full cohort maturation.
• Summarizes recent cardiac arrest trial (ICECAP), using 30-day ordinal scales and multiple imputation to predict 90-day outcomes and improve interim decision-making.
• Unpacks the importance of prior-data-driven modeling, simulation, and strict robustness checks in the construction of all predictive models used for interim adaptation.

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