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Utility-Based Dose Optimization Approaches for Multiple-Dose Randomized Trial Designs Accounting for Multiple

Gina D'Angelo1, Guannan Chen1,2, Di Ran1

  • 1Oncology Biometrics, Oncology R&D, AstraZeneca, Gaithersburg, Maryland, USA.

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|October 20, 2025
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Summary
This summary is machine-generated.

New methods, U-MET-m and CUI-MET, enhance oncology clinical trials by optimizing dose selection. These approaches effectively identify the optimal biological dose (OBD) using multiple endpoints and doses.

Keywords:
CUI‐METU‐MET‐mdose optimizationdose randomizationoptimal biological doseutility

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Area of Science:

  • Clinical Trials
  • Pharmacometrics
  • Biostatistics

Background:

  • Dose optimization is crucial in oncology clinical trials for determining the optimal biological dose (OBD).
  • Early-phase trials incorporating safety, efficacy, and biomarker data can facilitate OBD investigation.
  • Existing utility score-based approaches require extension to handle multiple endpoints and doses effectively.

Purpose of the Study:

  • To extend the utility score-based approach (U-MET) to accommodate multiple endpoints and doses in clinical trial designs.
  • To introduce U-MET-m for jointly accounting for multiple endpoints (≤3) and CUI-MET for marginally accounting for >3 endpoints.
  • To provide guidance on weight selection for U-MET-m and demonstrate the relationship between U-MET-m and CUI-MET.

Main Methods:

  • Developed U-MET-m (utility-based dose optimization for multiple-dose randomized trial designs) for ≤3 endpoints, using a utility score for joint endpoint consideration.
  • Developed CUI-MET (clinical utility index dose optimization approach) for >3 endpoints, using a utility index for marginal endpoint consideration.
  • Employed Bayesian inference within a hypothesis framework to compare utility metrics and identify the OBD, utilizing simulation studies and examples.

Main Results:

  • Both U-MET-m and CUI-MET demonstrated satisfactory operating characteristics for selecting the optimal biological dose (OBD).
  • Simulation studies and examples confirmed the effectiveness of the proposed methods compared to empirical designs.
  • A clear relationship was established between U-MET-m and CUI-MET, aiding in weight selection for U-MET-m.

Conclusions:

  • U-MET-m is recommended for dose comparison with ≤3 endpoints, while CUI-MET is recommended for >3 endpoints in selecting the OBD.
  • These novel approaches offer robust frameworks for dose optimization in oncology clinical trials.
  • The findings support the broader adoption of utility-based methods for efficient clinical trial design and dose selection.