ArticleBiometrics2025
Randomized optimal selection design for dose optimization.
Article in Biometrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Simulation-Calibrated Control of Type I Error Rate in One-Trial Approach with Dose Optimization in Oncology Drug Development.Therapeutic innovation & regulatory science · 2026Article
- Generalized Pairwise Comparisons in Dose Optimization Oncology Trials: Beyond Safety to Multi-outcome Dose Selection.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Hierarchical Composite Endpoints for Dose Optimization in Early Phase Oncology Trials.Pharmaceutical statisticsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The US 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, we propose a Randomized Optimal SElection (ROSE) design, which minimizes sample size while ensuring the probability of correct selection of the OBD at pre-specified 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 15-40 patients per dosage arm typically results in a percentage of correct selection of the optimal dose ranging from 60% to 70%.
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