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

ABSTRACT: 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 variab

SUBMITTER: Zhang S 

PROVIDER: S-EPMC12907573 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature