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BF-BOIN-ET: A Backfill Bayesian Optimal Interval Design Using Efficacy and Toxicity Outcomes for Dose Optimization.
Kentaro Takeda1, Jing Zhu2, Akihiro Hirakawa3
1Data Science, Astellas Pharma Global Development Inc., Northbrook, Illinois, USA.
Pharmaceutical Statistics
|March 10, 2025
Summary
This study introduces a new Bayesian design for cancer drug trials. The BF-BOIN-ET design improves optimal dose selection and reduces trial size and duration by backfilling patients at lower doses.
Area of Science:
- Clinical Trials
- Pharmacology
- Biostatistics
Background:
- Dose-finding trials are crucial for identifying optimal doses (OD) of novel anticancer agents.
- Unpredictable dose-toxicity and dose-efficacy relationships necessitate robust trial designs.
- FDA's Project Optimus encourages multi-dose comparisons for better dose optimization data.
Purpose of the Study:
- To propose a novel Bayesian optimal interval design for dose-finding trials.
- To incorporate both efficacy and toxicity outcomes in the design.
- To facilitate patient backfilling at lower doses while exploring higher doses.
Main Methods:
- Developed a Bayesian optimal interval design named BF-BOIN-ET.
- The design allows for patient backfilling at lower doses.
- Prioritizes dose-escalation to explore higher doses within the trial.
Main Results:
- The BF-BOIN-ET design demonstrated advantages in simulation studies.
- Showed improved percentage of correct OD selection compared to other designs.
- Reduced sample size and trial duration in realistic settings.
Conclusions:
- The proposed BF-BOIN-ET design offers an efficient approach to dose-finding for anticancer agents.
- It effectively balances efficacy and toxicity assessments.
- This design supports adaptive trial strategies recommended by regulatory bodies.
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