Abstract

Randomized controlled trials (RCT) have been the gold standard for evaluating medical treatments for many decades. Randomization reduces systematic biases resulting from treatment or patient selection, whereby the improvement in clinical outcomes may be attributed to the experimental therapy under study. However, RCTs are often criticized for requiring large sample sizes and taking a long time to complete. For newly diagnosed glioblastoma (GBM), the clinical trial landscape has seen little progress since the establishment of the standard of care (SOC) by Stupp. Given the urgent need for better therapies, it has been argued that data collected from patients treated with the SOC from past GBM trials can provide high-quality external control data to supplement concurrent control arm in future trials, thereby increasing drug development efficiency by reducing the number of patients treated with SOC. Herein we consider a new design approach that leverages historical control data in the design and analysis of phase 3 GBM trials. At the first stage, patients are randomized with an equal probability to standard (concurrent control) arm and experimental arm. An interim analysis entails an outcome comparison between the concurrent and external control arms. If comparability is established, the external control data are carried forward to be combined with concurrent control data at the second stage where the randomization ratio is adapted to favor the experimental therapy, thereby reducing the number of patients treated in the concurrent control arm. Using completed phase 3 GBM trials, we elucidate the potential gain in design efficiency and draw caution to scenarios where it may fall short on meeting statistical criteria. We highlight practical challenges in its implementation and conclude that the new method is not ready for definitive phase 3 GBM studies at the current time. This work represents a critical appraisal of this new concept in GBM.

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