Prognostic Model-Guided Randomization Improves Efficiency in Early-Phase Trials: Evidence From Surveys and Simulations.

Early-phase trials often struggle to detect treatment effects due to small sample sizes and substantial patient heterogeneity. While randomization is the standard for balancing treatment arms, many trials fail to account for key prognostic factors, potentially reducing statistical power and introducing bias. We surveyed 113 randomized oncology trials on ClinicalTrials.gov and found that established prognostic variables across cancer types, such as albumin, chloride, and Eastern Cooperative Oncology Group (ECOG) performance status, were frequently underutilized as randomization factors, despite being routinely collected at baseline. To address this, we evaluated a prognostic model-based randomization strategy using the Real-wOrld PROgnostic score (ROPRO), which integrates 27 baseline variables into a single continuous risk score across cancer indications. Using semi-synthetic simulations, we compared ROPRO-based randomization to ECOG randomization for detecting treatment effect across survival models, treatment effect sizes, and levels of patient heterogeneity. ROPRO consistently improved statistical power and reduced required sample sizes across treatment effect sizes (HR = 0.5, 0.6, 0.7) and survival models at different shapes. Power advantages ranged from +1 to +11 percentage points, with the greatest gains observed at moderate sample sizes. These findings support the use of prognostic model-informed randomization strategies in early-phase oncology trials, particularly in the context of FDA's Project Optimus, which emphasizes the need for finding optimal doses and regimens prior to registration trials.