Bayesian dose-finding in two treatment cycles based on the joint utility of efficacy and toxicity

• Autores: Juhee Lee, Peter F. Thall, Yuan Ji, Peter Müller
• Localización: Journal of the American Statistical Association, ISSN 0162-1459, Vol. 110, Nº 510, 2015, págs. 711-722
• Idioma: inglés
• DOI: 10.1080/01621459.2014.926815
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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