Posterior Predictive Design for Phase I Clinical Trials

• 成果类型: Article; Early Access
• 署名作者: Fu, Chenqi; Zhou, Shouhao; Lee, J. Jack
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health; University of Texas System; UTMD Anderson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2484044
• 发表日期: 2025
• 关键词: continual reassessment method; probability interval design; principles; cancer

摘要

Interval-based designs represent cutting-edge adaptive methodologies for phase I clinical trials to identify the maximum tolerated dose (MTD). These designs exhibit robust performance comparable to more intricate, model-based designs, and their pretabulated decision rule enables them to be implemented as simply as the conventional algorithm-based designs. In this paper, we introduce the posterior predictive (PoP) design, a novel interval-based design that leverages advanced Bayesian predictive hypothesis testing techniques for dose escalation and de-escalation. Our work moves beyond the existing model-assisted interval-based designs by achieving global optimality in dose transition. Theoretically, the global optimality ensures that the proposed design can consistently select the true MTD at an impressive convergence rate of n(-1/2). Through extensive simulation studies, we demonstrate that the PoP design yields substantial improvement in operating characteristics to identify MTD, thereby presenting a valuable upgrade to the popular interval-based designs in practice. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.