Dr. Scott Berry and Dr. Kert Viele discuss the origins and implementation of the “time machine” modeling approach, beginning with sports analytics and progressing to adaptive platform clinical trials. The episode focuses on how techniques for comparing athletes across eras translate into methodology for platform trials.

Key Highlights

• Sports analytics as foundation: Early work of modelling athlete comparisons across eras using bridging methodologies.
• Platform trial application: The time machine model in I-SPY 2 enabled efficient control allocation through overlapping arms over extended trial periods.
• Core modeling principles: Additive treatment effect assumptions and the necessity of sufficient temporal overlap for reliable era comparisons.
• Statistical implementation: Approaches include categorical era adjustment and Bayesian smoothing splines for modeling change over time.
• Limitations and disease specificity: In conditions with rapid clinical or epidemiologic change, such as COVID-19, non-concurrent controls are avoided due to high risk of era by treatment interaction.
• Regulatory and methodological distinction: The model leverages within-trial overlapping data collected under a unified protocol, contrasting sharply with external or historical controls.