论文标题
贝叶斯的一组最佳动态治疗方案和带有二元结果的智能的样本量确定
Bayesian Set of Best Dynamic Treatment Regimes and Sample Size Determination for SMARTs with Binary Outcomes
论文作者
论文摘要
连续分配，随机试验（SMART）的主要目标之一是找到最有效的设计嵌入式动态治疗方案。被称为多重比较的分析方法与最佳（MCB）进行了比较，可以比较动态治疗方案和识别频率设置中连续结果的一组最佳制度，从而直接解决了SMART的主要目标。在本文中，我们为具有二进制成果的智能者开发了对MCB的贝叶斯概括。此外，我们展示了如何选择样本量，以便用指定的功率筛选下嵌入的DTR。我们使用其确切的分布在不同的DTR之间比较了log-odds，而无需依赖分析或功率计算中的渐近态性。我们在两种智能设计下进行了广泛的仿真研究，并说明了我们方法对酒精和可卡因依赖性（参与）试验的适应性治疗的应用。
One of the main goals of sequential, multiple assignment, randomized trials (SMART) is to find the most efficacious design embedded dynamic treatment regimes. The analysis method known as multiple comparisons with the best (MCB) allows comparison between dynamic treatment regimes and identification of a set of optimal regimes in the frequentist setting for continuous outcomes, thereby, directly addressing the main goal of a SMART. In this paper, we develop a Bayesian generalization to MCB for SMARTs with binary outcomes. Furthermore, we show how to choose the sample size so that the inferior embedded DTRs are screened out with a specified power. We compare log-odds between different DTRs using their exact distribution without relying on asymptotic normality in either the analysis or the power calculation. We conduct extensive simulation studies under two SMART designs and illustrate our method's application to the Adaptive Treatment for Alcohol and Cocaine Dependence (ENGAGE) trial.
