ROSE: Randomized Optimal Selection Design for Dose Optimization

TL;DR

The paper introduces ROSE, a randomized selection design for dose optimization that reduces sample size and accurately identifies the optimal biological dose in clinical trials.

Contribution

It proposes a simple, two-stage randomized design that minimizes sample size while ensuring correct dose selection, aligning with FDA guidance.

Findings

ROSE design effectively identifies the OBD with 60-70% accuracy.

Sample sizes of 15-40 patients per arm are sufficient.

The two-stage approach allows early OBD selection, reducing trial duration.

Abstract

The U.S. Food and Drug Administration (FDA) launched Project Optimus to shift the objective of dose selection from the maximum tolerated dose to the optimal biological dose (OBD), optimizing the benefit-risk tradeoff. One approach recommended by the FDA's guidance is to conduct randomized trials comparing multiple doses. In this paper, using the selection design framework (Simon et al., 1985), we propose a randomized optimal selection (ROSE) design, which minimizes sample size while ensuring the probability of correct selection of the OBD at prespecified accuracy levels. The ROSE design is simple to implement, involving a straightforward comparison of the difference in response rates between two dose arms against a predetermined decision boundary. We further consider a two-stage ROSE design that allows for early selection of the OBD at the interim when there is sufficient evidence, further reducing the sample size. Simulation studies demonstrate that the ROSE design exhibits desirable operating characteristics in correctly identifying the OBD. A sample size of 15 to 40 patients per dosage arm typically results in a percentage of correct selection of the optimal dose ranging from 60% to 70%.