Clinical Research Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
College of Mathematics and Statistics, Shenzhen University, Shenzhen, 518060, China.
BMC Med Res Methodol. 2021 Dec 13;21(1):278. doi: 10.1186/s12874-021-01455-y.
With the emergence of molecularly targeted agents and immunotherapies, the landscape of phase I trials in oncology has been changed. Though these new therapeutic agents are very likely induce multiple low- or moderate-grade toxicities instead of DLT, most of the existing phase I trial designs account for the binary toxicity outcomes. Motivated by a pediatric phase I trial of solid tumor with a continuous outcome, we propose an adaptive generalized Bayesian optimal interval design with shrinkage boundaries, gBOINS, which can account for continuous, toxicity grades endpoints and regard the conventional binary endpoint as a special case.
The proposed gBOINS design enjoys convergence properties, e.g., the induced interval shrinks to the toxicity target and the recommended dose converges to the true maximum tolerated dose with increased sample size.
The proposed gBOINS design is transparent and simple to implement. We show that the gBOINS design has the desirable finite property of coherence and large-sample property of consistency. Numerical studies show that the proposed gBOINS design yields good performance and is comparable with or superior to the competing design.
随着分子靶向药物和免疫疗法的出现，肿瘤学的 I 期临床试验格局发生了变化。虽然这些新的治疗药物很可能会引起多种低或中度毒性，而不是 DLT，但大多数现有的 I 期试验设计都考虑了二元毒性结果。受小儿实体瘤连续结果的 I 期试验的启发，我们提出了一种具有收缩边界的自适应广义贝叶斯最优区间设计（gBOINS），它可以处理连续的、毒性等级终点，并将传统的二元终点视为特殊情况。
所提出的 gBOINS 设计具有收敛性质，例如，诱导的区间收缩到毒性目标，并且随着样本量的增加，推荐的剂量收敛到真实的最大耐受剂量。
所提出的 gBOINS 设计透明且易于实现。我们表明，gBOINS 设计具有理想的有限连贯性和大样本一致性。数值研究表明，所提出的 gBOINS 设计具有良好的性能，可与竞争设计相媲美或优于竞争设计。
