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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Randomized optimal selection design for dose optimization.

Shuqi Wang1, Ying Yuan1, Suyu Liu1

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.

Biometrics
|October 8, 2025
PubMed
Summary
This summary is machine-generated.

The US Food and Drug Administration (FDA) aims for optimal biological dose (OBD) selection. A new Randomized Optimal SElection (ROSE) design minimizes patient sample sizes for accurate OBD identification.

Keywords:
dose optimizationoptimal designrandomizationselection design

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Area of Science:

  • Clinical trial design
  • Pharmacometrics
  • Drug development

Background:

  • The US Food and Drug Administration (FDA) initiated Project Optimus to redefine cancer drug dose selection.
  • The goal is to move from maximum tolerated dose (MTD) to optimal biological dose (OBD) for improved benefit-risk balance.

Purpose of the Study:

  • To propose a novel Randomized Optimal SElection (ROSE) design for identifying the optimal biological dose (OBD).
  • To minimize sample size requirements in clinical trials while maintaining high accuracy in OBD selection.

Main Methods:

  • The study introduces the Randomized Optimal SElection (ROSE) design within a selection design framework.
  • ROSE involves comparing response rates between dose arms against a decision boundary.
  • A two-stage ROSE design is also presented for potential early OBD selection.

Main Results:

  • The ROSE design effectively minimizes sample size for accurate OBD identification.
  • Simulation studies show favorable operating characteristics for the ROSE design.
  • Sample sizes of 15-40 patients per arm yield 60%-70% correct selection of the optimal dose.

Conclusions:

  • The proposed ROSE design offers an efficient method for optimal biological dose selection.
  • This approach supports the FDA's Project Optimus initiative by optimizing clinical trial efficiency.
  • ROSE facilitates better benefit-risk assessment in drug development through precise dose selection.