Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity

• 成果类型: Article
• 署名作者: Lee, Juhee; Thall, Peter F.; Ji, Yuan; Mueller, Peter
• 署名单位: University System of Ohio; Ohio State University; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; University of Texas Austin
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.926815
• 发表日期: 2015
• 页码: 711-722
• 关键词: 2-stage randomization designs; MARGINAL STRUCTURAL MODELS; clinical-trials; phase-i/ii; survival distributions; treatment strategies; treatment policies; Causal Inference; mean models; schedule

摘要

This article proposes a phase I/II clinical trial design 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. Supplementary materials for this article are available online.