Backfilling Patients in Phase I Dose-Escalation Trials Using Bayesian Optimal Interval Design (BOIN).
Yixuan Zhao1, Ying Yuan2, Edward L Korn3
1Department of Biostatistics, University of Texas Health Science Center, Houston, Texas.
Summary
This study introduces a new backfilling method for dose-escalation trials, improving patient safety and data collection. The backfilling Bayesian Optimal Interval design (BOIN) enhances dose optimization without extending trial length.
Area of Science:
- Clinical Trials
- Biostatistics
- Pharmacology
Background:
- Backfilling in dose-escalation trials is gaining interest for collecting more safety and efficacy data.
- Current backfilling practices lack standardization and rigorous definition, potentially leading to suboptimal patient allocation.
- Careful design is crucial to avoid treating patients with ineffective or subtherapeutic doses.
Purpose of the Study:
- To propose a principled approach for integrating backfilling into the Bayesian Optimal Interval design (BOIN).
- To ensure additional patients in dose-escalation trials are treated at doses with observed activity.
- To enhance the utility of backfilling for dose optimization and MTD selection.
Main Methods:
- Developed a novel backfilling Bayesian Optimal Interval design (BF-BOIN).
- Integrated dose-escalation and backfilling data within the BOIN framework.
- Conducted simulation studies to evaluate the BF-BOIN design's performance.
Main Results:
- The BF-BOIN design effectively generates additional data for dose optimization.
- MTD identification accuracy is maintained with the proposed backfilling approach.
- Patient safety is improved without increasing overall trial duration.
Conclusions:
- The proposed BF-BOIN design offers a practical and effective method for incorporating backfilling into dose-escalation studies.
- This approach enhances data collection for dose optimization and MTD selection while prioritizing patient safety.
- BF-BOIN represents a significant advancement in the design and execution of adaptive clinical trials.
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