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

TL;DR

This paper introduces a utility score framework for dose optimization in oncology, integrating efficacy and safety endpoints, with formulas for sample size and bias analysis, supported by simulations and software tools.

Contribution

It presents a novel utility score framework with analytical sample size formulas and bias characterization methods for dose optimization studies.

Findings

Sample size formulas achieve target PCS

Bias and Type I error formulas closely match empirical results

Framework effectively guides dose selection balancing efficacy and safety

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.