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Prognostic Model-Guided Randomization Improves Efficiency in Early-Phase Trials: Evidence From Surveys and
Sihong Zhang1, Justin Zhao1, Yanguang Cao1,2
1Division of Pharmacotherapy and Experimental Therapeutics, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Clinical and Translational Science
|February 16, 2026
Summary
Improving early-phase oncology trials requires better randomization. Using prognostic models like ROPRO, instead of just ECOG status, enhances statistical power and reduces sample size needs for detecting treatment effects.
Area of Science:
- Oncology
- Clinical Trials
- Biostatistics
Background:
- Early-phase oncology trials face challenges in detecting treatment effects due to small sample sizes and patient heterogeneity.
- Standard randomization often underutilizes key prognostic factors, potentially reducing statistical power and introducing bias.
- Established prognostic variables like ECOG performance status are frequently underutilized in current trial designs.
Purpose of the Study:
- To evaluate a prognostic model-based randomization strategy using the Real-wOrld PROgnostic score (ROPRO).
- To compare the statistical power and sample size requirements of ROPRO-based randomization versus ECOG-based randomization.
- To support the use of prognostic model-informed randomization in early-phase oncology trials, aligning with FDA's Project Optimus goals.
Main Methods:
- Surveyed 113 randomized oncology trials on ClinicalTrials.gov to assess the utilization of prognostic factors.
- Developed the Real-wOrld PROgnostic score (ROPRO) integrating 27 baseline variables into a single risk score.
- Employed semi-synthetic simulations to compare ROPRO-based randomization with ECOG randomization across various survival models and treatment effect sizes.
Main Results:
- ROPRO-based randomization consistently improved statistical power compared to ECOG randomization.
- The ROPRO strategy reduced the required sample sizes for detecting treatment effects.
- Power advantages ranged from +1 to +11 percentage points, with significant gains at moderate sample sizes.
Conclusions:
- Prognostic model-informed randomization strategies, such as using ROPRO, enhance statistical power in early-phase oncology trials.
- This approach can lead to more efficient trial designs by reducing sample size requirements.
- Implementing advanced randomization methods is crucial for optimizing dose and regimen selection prior to registration trials.
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