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

被引:47
作者
Lee, Juhee [1 ]
Thall, Peter F. [2 ]
Ji, Yuan [3 ]
Mueller, Peter [4 ]
机构
[1] Ohio State Univ, Dept Stat, Columbus, OH 43210 USA
[2] Univ Texas MD Anderson Canc Ctr, Dept Biostat, Houston, TX 77230 USA
[3] North Shore Univ Hlth Syst, Ctr Clin & Res Informat, Evanston, IL 60201 USA
[4] Univ Texas Austin, Dept Math, Austin, TX 78712 USA
关键词
Adaptive design; Bayesian design; Dynamic treatment regime; Latent probit model; Phase I-II clinical trial; Q-learning; 2-STAGE RANDOMIZATION DESIGNS; MARGINAL STRUCTURAL MODELS; CLINICAL-TRIALS; PHASE-I/II; SURVIVAL DISTRIBUTIONS; TREATMENT STRATEGIES; TREATMENT POLICIES; CAUSAL INFERENCE; MEAN MODELS; SCHEDULE;
D O I
10.1080/01621459.2014.926815
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
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.
引用
收藏
页码:711 / 722
页数:12
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