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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Updated: Feb 17, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
clinical trialprognostic modelproject optimusrandomization

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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.