Overview
There is great interest in using external control arms (ECAs) with real-world data (RWD) in oncology studies. However, challenges in accurately measuring commonly used oncology endpoints like progression-free survival (PFS) remain a concern, as errors can arise from how and when disease assessments occur in real-world settings.
This study aims to identify the key contributing factors to measurement differences between real-world PFS and trial PFS via simulation analyses. The study distinguishes between misclassification bias and surveillance bias, identifying both misclassification of events and irregular assessment frequencies as contributors to differences between real-world and trial endpoints. The research indicates varying impacts attributable to the type of measurement error, and demonstrates the utility of simulation studies in understanding the impact of endpoint measurement differences between RWD and clinical trials.
Why this matters
Randomized controlled trials (RCTs) remain the gold standard for researching oncology treatments. However, there are many situations in which an RCT may not be feasible, including in enrollment of trial participants (in cases of rare disease, or other highly specialized populations) or lack of clinical equipoise. Deepening our understanding of appropriate use of external control arms (ECAs) for comparison to a single-arm trial has the potential to de-risk and accelerate clinical trials when a RCT is not feasible, potentially bringing treatments to patients sooner.