A Utility Score Framework for Dose Optimization Studies with Binary Efficacy-Safety Endpoints: Sample Size Determination and Bias Characterization

2026-03-16

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements

Abstract

The FDA's Project Optimus initiative emphasizes patient-centered dose selection in oncology that balances efficacy and safety. We develop a framework for randomized dose optimization studies that uses clinically interpretable utility scores to integrate binary efficacy and safety endpoints and select the optimal dose for a follow-on confirmatory trial. The framework provides: (i) a systematic method for eliciting utility scores that reflect clinical priorities; (ii) closed-form sample size formulas to achieve prespecified Probabilities of Correct Selection (PCS) under clinically relevant scenarios; and (iii) analytical expressions characterizing the propagation of selection-induced bias to confirmatory trials, including time-to-event endpoints correlated with the selection endpoint. Extensive simulations (10^6 replications per scenario) confirm that the sample size methods achieve target PCS and that the bias and Type I error formulas closely match empirical estimates. An R package DoseOptDesign and an interactive Shiny application are publicly available.

Problem

• dose optimization
• efficacy-safety tradeoff
• binary endpoints
• optimal dose selection
• patient-centered dose selection

Innovation

• utility score
• dose optimization
• sample size determination
• selection bias
• binary endpoints