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<p>The one-trial approach [<CitationRef CitationID="CR8">8</CitationRef>] has emerged as a pivotal strategy for streamlining oncology drug development by allowing a single study to support both accelerated approval (AA) and regular approval (RA). The <Emphasis Type="Underline">i</Emphasis>ntegrating <Emphasis Type="Underline">D</Emphasis>ose <Emphasis Type="Underline">O</Emphasis>ptimization with <Emphasis Type="Underline">O</Emphasis>ne-<Emphasis Type="Underline">T</Emphasis>rial <Emphasis Type="Underline">A</Emphasis>pproach (iDOOTA) framework extends this paradigm by enabling a single randomized study to both identify an optimal biological dose (OBD) and provide confirmatory evidence on short- and long-term clinical outcomes, such as ORR and OS. In this paper, we propose a simulation-based calibration strategy to control the type I error rate induced by dose selection within the iDOOTA framework. A numerical case study is presented to illustrate the key features and practical implementation of the proposed method. In addition, several operational considerations relevant to the iDOOTA framework are discussed. As with all innovative regulatory strategies, it is advisable to seek FDA agreement during the study design phase to ensure alignment and facilitate subsequent regulatory review.</p>

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Simulation-Calibrated Control of Type I Error Rate in One-Trial Approach with Dose Optimization in Oncology Drug Development

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Zhao Yang

摘要

The one-trial approach [8] has emerged as a pivotal strategy for streamlining oncology drug development by allowing a single study to support both accelerated approval (AA) and regular approval (RA). The integrating Dose Optimization with One-Trial Approach (iDOOTA) framework extends this paradigm by enabling a single randomized study to both identify an optimal biological dose (OBD) and provide confirmatory evidence on short- and long-term clinical outcomes, such as ORR and OS. In this paper, we propose a simulation-based calibration strategy to control the type I error rate induced by dose selection within the iDOOTA framework. A numerical case study is presented to illustrate the key features and practical implementation of the proposed method. In addition, several operational considerations relevant to the iDOOTA framework are discussed. As with all innovative regulatory strategies, it is advisable to seek FDA agreement during the study design phase to ensure alignment and facilitate subsequent regulatory review.