Design and Sample Size Determination for Multiple-dose Randomized Phase II Trials for Dose Optimization

TL;DR

This paper introduces the MERIT design, a statistical framework for determining sample size in multiple-dose phase II trials aimed at optimizing dose selection for targeted therapies, aligning with FDA guidance.

Contribution

It develops a novel sample size determination method for dose optimization trials, incorporating toxicity and efficacy, with pre-calculated decision boundaries for practical implementation.

Findings

MERIT design achieves desirable operating characteristics in simulations

The method generalizes traditional error definitions for dose optimization

Software implementation is provided for easy adoption

Abstract

The conventional more-is-better dose selection paradigm, which targets the maximum tolerated dose (MTD), is not suitable for the development of targeted therapies and immunotherapies as the efficacy of these novel therapies may not increase with the dose. The U.S. Food and Drug Administration (FDA) has launched Project Optimus "to reform the dose optimization and dose selection paradigm in oncology drug development", and recently published a draft guidance on dose optimization, which outlines various approaches to achieve this goal. One highlighted approach involves conducting a randomized phase II trial following the completion of a phase I trial, where multiple doses (typically including the MTD and one or two doses lower than the MTD) are compared to identify the optimal dose that maximizes the benefit-risk tradeoff. This paper focuses on the design of such a multiple-dose randomized trial, specifically the determination of the sample size. We propose a MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy) design that can be easily implemented with pre-calculated decision boundaries included in the protocol. We generalized the standard definitions of type I error and power to accommodate the unique characteristics of dose optimization and derived a decision rule along with an algorithm to determine the optimal sample size. Simulation studies demonstrate that the resulting MERIT design has desirable operating characteristics. To facilitate the implementation of the MERIT design, we provide software, available at www.trialdesign.org.