Adaptive design for identifying maximum tolerated dose early to accelerate dose-finding trial

TL;DR

This paper introduces a Bayesian adaptive design that identifies the maximum tolerated dose early in phase I trials, significantly reducing study duration while maintaining accuracy.

Contribution

The paper presents a novel adaptive Bayesian design for early MTD identification, validated through actual trial application and simulation studies.

Findings

Early MTD identification confirmed in actual trial.

Study duration reduced by about 50% with the new design.

Maintained accuracy comparable to traditional methods.

Abstract

Purpose: The early identification of maximum tolerated dose (MTD) in phase I trial leads to faster progression to a phase II trial or an expansion cohort to confirm efficacy. Methods: We propose a novel adaptive design for identifying MTD early to accelerate dose-finding trials. The early identification of MTD is determined adaptively by dose-retainment probability using a trial data via Bayesian analysis. We applied the early identification design to an actual trial. A simulation study evaluates the performance of the early identification design. Results: In the actual study, we confirmed the MTD could be early identified and the study period was shortened. In the simulation study, the percentage of the correct MTD selection in the early identification Keyboard and early identification Bayesian optimal interval (BOIN) designs was almost same from the non-early identification version. The early identification Keyboard and BOIN designs reduced the study duration by about 50% from the model-assisted designs. In addition, the early identification Keyboard and BOIN designs reduced the study duration by about 20% from time-to-event model-assisted designs. Conclusion: We proposed the early identification of MTD maintaining the accuracy to be able to short the study period.