On the latest episode of "In the Interim...", Dr. Scott Berry and Dr. Kert Viele deliver a focused, technical analysis of statistical bias when stopping trials early. This episode clarifies the definition of bias, detailed within the context of interim analyses, emphasizing the empirical consequences of different stopping rules. The discussion addresses common misconceptions around interpretation as well as including the mathematical rationale for averaging across all trial outcomes, and the error of restricting bias estimates to only successful (early-stopped) trials. The hosts present a detailed critique of Bassler et al. (JAMA 2010), highlighting methodological flaws and misinterpretations of comparisons between truncated and non-truncated studies. Simulation is positioned as the primary tool for quantifying bias, with contextual examples illustrating the manageable magnitude of bias. Regulatory expectations are summarized, referencing formal FDA and ICH guidance on adaptive design bias assessment. The DAWN trial is cited as a real-world example where early stopping accelerated patient benefit.
Key Highlights
Definition and quantification of bias in early-stopped clinical trials
Mathematical examples demonstrating bias magnitude in fixed and adaptive group sequential designs
Detailed critique of the methodology and conclusions in Bassler et al. (JAMA 2010)
Discussion correcting common misunderstandings in bias estimation and selective reporting
Simulation as a decisive tool for precise bias estimation
Regulatory context including FDA guidance and ICH E20 draft guidance
Reference to DAWN trial as evidence of practical benefits of early stopping
