Zhao Saijun, Thall Peter F, Yuan Ying, Lee Juhee, Msaouel Pavlos, Zang Yong
Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.
Department of Biostatistics, M.D. Anderson Cancer Center, Houston, TX 77030, USA.
Biometrics. 2025 Jul 3;81(3). doi: 10.1093/biomtc/ujaf043.
A new family of precision Bayesian dose optimization designs, PGen I-II, based on early efficacy, early toxicity, and long-term time to treatment failure is proposed. A PGen I-II design refines a Gen I-II design by accounting for patient heterogeneity characterized by subgroups that may be defined by prognostic levels, disease subtypes, or biomarker categories. The design makes subgroup-specific decisions, which may be to drop an unacceptably toxic or inefficacious dose, randomize patients among acceptable doses, or identify a best dose in terms of treatment success defined in terms of time to failure over long-term follow-up. A piecewise exponential distribution for failure time is assumed, including subgroup-specific effects of dose, response, and toxicity. Latent variables are used to adaptively cluster subgroups found to have similar dose-outcome distributions, with the model simplified to borrow strength between subgroups in the same cluster. Guidelines and user-friendly computer software for implementing the design are provided. A simulation study is reported that shows the PGen I-II design is superior to similarly structured designs that either assume patient homogeneity or conduct separate trials within subgroups.
提出了一种基于早期疗效、早期毒性和长期治疗失败时间的精密贝叶斯剂量优化设计新家族PGen I-II。PGen I-II设计通过考虑以预后水平、疾病亚型或生物标志物类别定义的亚组所表征的患者异质性来完善I-II期设计。该设计做出亚组特异性决策，可能是放弃毒性不可接受或无效的剂量，在可接受剂量之间随机分配患者，或者根据长期随访中失败时间定义的治疗成功情况确定最佳剂量。假设失败时间服从分段指数分布，包括剂量、反应和毒性的亚组特异性效应。使用潜在变量对发现具有相似剂量-结果分布的亚组进行自适应聚类，简化模型以在同一聚类中的亚组之间借用强度。提供了实施该设计的指南和用户友好的计算机软件。报告了一项模拟研究，表明PGen I-II设计优于那些要么假设患者同质性要么在亚组内进行单独试验的结构相似的设计。
