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Updated: Jun 6, 2026
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Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition
Published on: March 11, 2021
CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose,
Fanni Zhang1, Kristine Broglio1, Michael Sweeting2
1Oncology Biometrics, AstraZeneca, Gaithersburg, Maryland, USA.
Pharmaceutical Statistics
|June 5, 2026
Summary
This study introduces a new framework for optimizing cancer drug doses by balancing benefits and risks across multiple outcomes. The Clinical Utility Index (CUI) method aids decision-making in early-phase oncology trials.
Area of Science:
- Oncology
- Biostatistics
- Clinical Trial Design
Background:
- Dose optimization in oncology trials has evolved from finding maximum tolerated dose to identifying the Optimal Biological Dose (OBD).
- Existing advanced dose-finding methods may not be suitable for small sample sizes in randomized dose optimization studies with multiple endpoints.
- There is a need for methods that integrate multiple clinical attributes to balance therapeutic benefits and risks.
Purpose of the Study:
- To propose a novel Clinical Utility Index (CUI) based analysis and decision framework (CUI-MET) for dose optimization in multiple-dose, multiple-outcome randomized trials.
- To provide a flexible and accessible solution for informed decision-making in early-phase oncology trials.
- To integrate patient-relevant outcomes into the dose selection process.
Main Methods:
- Developed a framework integrating multiple binary endpoints into a combined CUI for each dose level by weighting endpoints.
- Estimated marginal summaries of individual endpoints empirically or via parametric dose-response models.
- Utilized bootstrap analysis for confidence intervals and to evaluate the robustness of dose selection. Implemented in an R Shiny application.
Main Results:
- The CUI-MET framework computes a utility score for each dose by combining estimated endpoint probabilities using weights.
- The optimal dose is selected as the one with the highest score within a clinically acceptable set.
- The framework demonstrated flexibility in model selection and endpoint weighting schemes.
Conclusions:
- CUI-MET offers a flexible and accessible solution for dose optimization in early-phase oncology trials by integrating multiple endpoints into a single utility index.
- The framework supports informed decision-making by providing confidence intervals and probability estimates for dose selection.
- User-friendly visualizations enhance usability and facilitate the integration of patient-relevant outcomes.
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