ROSE: Randomized Optimal Selection Design for Dose Optimization

2025-05-06

AI Summary

In response to the FDA’s Project Optimus initiative, this study addresses the paradigm shift from traditional maximum tolerated dose (MTD) estimation to precise optimal biological dose (OBD) selection. Method: We propose a novel randomized selection design—single- or two-stage ROSE—built upon Simon’s selection framework. It employs response-rate differences relative to prespecified boundaries for simple, interpretable decision-making and incorporates adaptive early-stopping rules to enable mid-trial OBD locking. Contribution/Results: Under prespecified correct-selection probabilities of 60%–70%, the design requires only 15–40 patients per dose level, substantially reducing sample size. Simulation studies demonstrate robust statistical power and excellent operating characteristics. ROSE provides an efficient, transparent, and regulatory-friendly paradigm for OBD selection, facilitating practical implementation in modern oncology trials.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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%.

Problem

• Shifting dose selection from maximum tolerated to optimal biological dose
• Minimizing sample size while ensuring accurate optimal dose selection
• Implementing a randomized design for comparing multiple dose arms

Innovation

• Randomized optimal selection design for dose optimization
• Two-stage design for early OBD selection
• Minimizes sample size with prespecified accuracy