Leveraging External Control Data in the Design and Analysis of Neuro-Oncology Trials: Pearls and Perils.

Randomized controlled trials (RCT) have been the gold standard for evaluating medical treatments for many decades but they are often criticized for requiring large sample sizes. For newly-diagnosed glioblastoma (GBM), the clinical trial landscape has seen little progress since the establishment of the standard of care (known as the "Stupp" regimen). Given the urgent need for better therapies, it has been argued that data collected from patients treated with the standard regimen can provide high-quality external control data to supplement or replace concurrent control arm in future RCT. The goal of this Review is to provide an in-depth appraisal of the use of external control data in the context of RCT. We describe several clinical trial designs with particular attention to how external information is utilized, and address common fallacies that may lead to inappropriate adoptions of external control data. Using two completed GBM trials, we illustrate the use of an assessment tool that lays out a blueprint for assembling a high-quality external control dataset. Using statistical simulations, we draw caution from scenarios where these approaches can fall short on controlling the type I error rate. Practical and statistical challenges associated with implementing these designs are also discussed.

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