Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Stat Med. 2022 Mar 15;41(6):1059-1080. doi: 10.1002/sim.9316. Epub 2022 Jan 25.
We propose an adaptive design for early-phase drug-combination cancer trials with the goal of estimating the maximum tolerated dose (MTD). A nonparametric Bayesian model, using beta priors truncated to the set of partially ordered dose combinations, is used to describe the probability of dose limiting toxicity (DLT). Dose allocation between successive cohorts of patients is estimated using a modified continual reassessment scheme. The updated probabilities of DLT are calculated with a Gibbs sampler that employs a weighting mechanism to calibrate the influence of data vs the prior. At the end of the trial, we recommend one or more dose combinations as the MTD based on our proposed algorithm. We apply our method to a Phase I clinical trial of CB-839 and Gemcitabine that motivated this nonparametric design. The design operating characteristics indicate that our method is comparable with existing methods.
我们提出了一种用于早期药物联合癌症试验的自适应设计，旨在估计最大耐受剂量（MTD）。使用贝叶斯模型，使用截断到部分有序剂量组合集的β先验来描述剂量限制毒性（DLT）的概率。使用改良的连续评估方案估计连续患者队列之间的剂量分配。使用 Gibbs 抽样器更新 DLT 的概率，该抽样器使用加权机制来校准数据与先验的影响。在试验结束时，我们根据建议的算法推荐一个或多个剂量组合作为 MTD。我们将我们的方法应用于 CB-839 和吉西他滨的 I 期临床试验，该试验激发了这种非参数设计。设计操作特性表明，我们的方法与现有方法相当。
