Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Xuemin Gu, Cong Xu, Lei Xu, Ying Yu

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.

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

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.
Paper Structure (64 sections, 6 theorems, 88 equations, 7 tables)

This paper contains 64 sections, 6 theorems, 88 equations, 7 tables.

Table of Contents

  1. Introduction
  2. Utility Score Framework and Dose Selection Design
  3. Overview and Motivation
  4. Efficacy-Safety Outcomes and Utility Framework
  5. Utility Score Specification: The Margin-Based Approach
  6. Clinical Motivation: Utility Independence.
  7. Derivation of Utility Scores.
  8. Alternative Specifications.
  9. Dose Selection Rule
  10. Sample Size Determination
  11. Clinical Scenarios for Sample Size Calculation
  12. Interpretation of Scenarios.
  13. Symmetric Utility Differences.
  14. Parameter Estimation from Historical Data
  15. Constraints on the Correlation Parameter.
  16. ...and 49 more sections

Key Result

Lemma A.1

For $Z_H, Z_L \stackrel{\mathrm{iid}}{\sim} N(0,1)$ and any threshold $k \in \mathbb{R}$:

Theorems & Definitions (15)

  • Lemma A.1: Truncated Selection Expectation
  • proof
  • Theorem A.2: Selection Bias Under Utility-Based Selection
  • proof
  • Corollary A.3: Maximum Selection Bias
  • proof
  • Proposition A.4: Bias Propagation Chain
  • proof
  • Proposition A.5: Type I Error for TTE Confirmatory Tests
  • proof
  • ...and 5 more