Abstract:Model-assisted interval designs such as the Keyboard design are transparent and easy to implement in phase I oncology trials. However, interim decisions based solely on data from the current dose may overlook informative signals from neighbouring doses, leading to unnecessary escalation or de-escalation. We propose the shared Keyboard design, a Bayesian model-assisted design that replaces the independent beta--binomial updating scheme at each dose with a posterior induced by a Beta kernel process using kernel-weighted pseudo-counts. The design preserves the decision structure of the Keyboard design while enabling controlled borrowing across nearby doses. To prioritise overdose control, we propose an asymmetric kernel that assigns greater weight to toxicities observed at higher doses during escalation. We further extend the proposed design to accommodate adaptive dose insertion when the initial dose grid is inadequate and time-to-event outcomes when late-onset toxicities are present. Extensive simulation studies demonstrate substantial improvements in both accuracy and safety for identifying the maximum tolerated dose. In settings involving dose insertion, the proposed design identifies inserted target doses more effectively than adaptive dose modification while maintaining a comparable modification rate.
| Comments: | 42 pages, 14 figures, 9 tables |
| Subjects: | Applications (stat.AP); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.25043 [stat.AP] |
| (or arXiv:2605.25043v1 [stat.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2605.25043 arXiv-issued DOI via DataCite (pending registration) |
From: Jiangyan Zhao [view email]
[v1]
Sun, 24 May 2026 12:39:00 UTC (273 KB)
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