Department of Statistics, Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
Department of Biostatistics, M.D. Anderson Cancer Center, Houston, Texas, USA.
Biometrics. 2023 Sep;79(3):2458-2473. doi: 10.1111/biom.13738. Epub 2022 Sep 19.
A Bayesian design is proposed for randomized phase II clinical trials that screen multiple experimental treatments compared to an active control based on ordinal categorical toxicity and response. The underlying model and design account for patient heterogeneity characterized by ordered prognostic subgroups. All decision criteria are subgroup specific, including interim rules for dropping unsafe or ineffective treatments, and criteria for selecting optimal treatments at the end of the trial. The design requires an elicited utility function of the two outcomes that varies with the subgroups. Final treatment selections are based on posterior mean utilities. The methodology is illustrated by a trial of targeted agents for metastatic renal cancer, which motivated the design methodology. In the context of this application, the design is evaluated by computer simulation, including comparison to three designs that conduct separate trials within subgroups, or conduct one trial while ignoring subgroups, or base treatment selection on estimated response rates while ignoring toxicity.
提出了一种贝叶斯设计，用于针对基于有序分类毒性和反应的活性对照比较的多个实验治疗进行随机二期临床试验筛选。基础模型和设计考虑了以有序预后亚组为特征的患者异质性。所有决策标准都是特定于亚组的，包括不安全或无效治疗的中期淘汰规则，以及试验结束时选择最佳治疗的标准。该设计需要一个与亚组相关的两个结果的诱发效用函数。最终的治疗选择基于后验均值效用。该方法通过转移性肾细胞癌的靶向药物试验进行了说明，该试验激发了设计方法。在这种情况下，通过计算机模拟对设计进行了评估，包括与在亚组内进行单独试验、或进行一次试验而忽略亚组、或基于毒性忽略反应率进行治疗选择的三种设计进行比较。
