Seamless Phase 2-3 Design: A Useful Strategy to Reduce the Sample Size for Dose Optimization

TL;DR

This paper explores seamless phase 2-3 designs for dose optimization in oncology, showing they can reduce sample sizes by about 22% while maintaining statistical validity, thus offering a more efficient alternative to traditional methods.

Contribution

It systematically investigates and describes four types of seamless phase 2-3 dose-optimization designs, demonstrating their efficiency and operational considerations in clinical trials.

Findings

Dose-optimization phase 2-3 designs control type I error rates.

They achieve 16.6% to 27.3% sample size reduction.

Designs are suitable for different clinical settings.

Abstract

The traditional more-is-better dose selection paradigm, developed based on cytotoxic chemotherapeutics, is often problematic When applied to the development of novel molecularly targeted agents (e.g., kinase inhibitors, monoclonal antibodies, and antibody-drug conjugates). The US Food and Drug Administration (FDA) initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development and call for more attention to benefit-risk consideration. We systematically investigated the operating characteristics of the seamless phase 2-3 design as a strategy for dose optimization, where in stage 1 (corresponding to phase 2) patients are randomized to multiple doses, with or without a control; and in stage 2 (corresponding to phase 3) the efficacy of the selected optimal dose is evaluated with a randomized concurrent control or historical control. Depending on whether the concurrent control is included and the type of endpoints used in stages 1 and 2, we describe four types of seamless phase 2-3 dose-optimization designs, which are suitable for different clinical settings. The statistical and design considerations that pertain to dose optimization are discussed. Simulation shows that dose optimization phase 2-3 designs are able to control the familywise type I error rates and yield appropriate statistical power with substantially smaller sample size than the conventional approach. The sample size savings range from 16.6% to 27.3%, depending on the design and scenario, with a mean savings of 22.1%. Due to the interim dose selection, the phase 2-3 dose-optimization design is logistically and operationally more challenging, and should be carefully planned and implemented to ensure trial integrity.