Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity.
Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity.
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DOI：
10.1080/01621459.2014.926815
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发表时间：
2015-06-01
影响因子：
3.7
通讯作者：
Müller P
中科院分区：
文献类型：
--
作者：
Lee J;Thall PF;Ji Y;Müller P
提出了一种I/II期临床试验设计，根据每个周期的联合二元疗效和毒性结果，自适应地动态优化每个患者在两个治疗周期中的剂量。假设剂量-结果模型包括贝叶斯分层潜在变量结构，以诱导结果之间的关联，并促进后验计算。基于基于模型的目标函数的后验选择每个周期中的剂量，类似于强化学习或Q学习函数，根据每个周期中联合结果的数值效用定义。对于每个患者，该程序输出两个动作的序列，每个动作用于每个周期，每个动作是以所选剂量治疗患者或不治疗的决定。周期2动作取决于个体患者的周期1剂量和结果。此外，决策基于使用其他患者数据的后验推理，因此所提出的方法在患者内和患者之间都是自适应的。仿真研究的方法，包括比较传统的3+3算法的两个周期的扩展，不断重新评估的方法，基于贝叶斯模型的设计，和鲁棒性评估。
A phase I/II clinical trial design is proposed for adaptively and dynamically optimizing each patient's dose in each of two cycles of therapy based on the joint binary efficacy and toxicity outcomes in each cycle. A dose-outcome model is assumed that includes a Bayesian hierarchical latent variable structure to induce association among the outcomes and also facilitate posterior computation. Doses are chosen in each cycle based on posteriors of a model-based objective function, similar to a reinforcement learning or Q-learning function, defined in terms of numerical utilities of the joint outcomes in each cycle. For each patient, the procedure outputs a sequence of two actions, one for each cycle, with each action being the decision to either treat the patient at a chosen dose or not to treat. The cycle 2 action depends on the individual patient's cycle 1 dose and outcomes. In addition, decisions are based on posterior inference using other patients’ data, and therefore the proposed method is adaptive both within and between patients. A simulation study of the method is presented, including comparison to two-cycle extensions of the conventional 3+3 algorithm, continual reassessment method, and a Bayesian model-based design, and evaluation of robustness.