Shared Keyboard: An improved Bayesian design for phase I clinical trials via Beta kernel process

2026-05-24

AI Summary

This work proposes the shared Keyboard design, a novel model-assisted approach for phase I oncology trials that overcomes the limitation of conventional Keyboard designs by incorporating information from neighboring doses. By introducing a Beta kernel process, the method enables controlled borrowing of strength across doses through kernel-weighted pseudo-counts, while preserving the original decision framework. It further supports asymmetric kernels to enhance overdose control. The approach naturally extends to adaptive dose insertion and time-to-event toxicity outcomes. Simulation studies demonstrate that the shared Keyboard design significantly improves both accuracy and safety in identifying the maximum tolerated dose compared to existing methods, achieves higher efficiency in locating the target dose under dose-insertion scenarios, and maintains comparable modification rates.

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.

Problem

• phase I clinical trials
• dose finding
• maximum tolerated dose
• neighboring dose information
• late-onset toxicities

Innovation

• shared Keyboard design
• Beta kernel process
• model-assisted design
• adaptive dose insertion
• late-onset toxicity