BARD: A seamless two-stage dose optimization design integrating backfill and adaptive randomization

TL;DR

BARD is a novel seamless two-stage dose optimization design that reduces sample size and trial duration by integrating backfilling and covariate-adaptive randomization, improving accuracy in identifying the optimal dose.

Contribution

This paper introduces BARD, a new dose optimization method that combines backfilling and adaptive randomization to enhance efficiency and accuracy over traditional two-stage designs.

Findings

Reduces sample size compared to traditional methods.

Maintains covariate balance in randomization.

Improves accuracy in identifying the optimal dose.

Abstract

One common approach for dose optimization is a two-stage design, which initially conducts dose escalation to identify the maximum tolerated dose (MTD), followed by a randomization stage where patients are assigned to two or more doses to further assess and compare their risk-benefit profiles to identify the optimal dose. A limitation of this approach is its requirement for a relatively large sample size. To address this challenge, we propose a seamless two-stage design, BARD (Backfill and Adaptive Randomization for Dose Optimization), which incorporates two key features to reduce sample size and shorten trial duration. The first feature is the integration of backfilling into the stage 1 dose escalation, enhancing patient enrollment and data generation without prolonging the trial. The second feature involves seamlessly combining patients treated in stage 1 with those in stage 2, enabled by covariate-adaptive randomization, to inform the optimal dose and thereby reduce the sample size. Our simulation study demonstrates that BARD reduces the sample size, improves the accuracy of identifying the optimal dose, and maintains covariate balance in randomization, allowing for unbiased comparisons between doses. BARD designs offer an efficient solution to meet the dose optimization requirements set by Project Optimus, with software freely available at www.trialdesign.org.