The sequential multiple assignment randomized trial (SMART) is the gold standard trial design to generate data for the evaluation of multi-stage treatment regimes. As with conventional (single-stage) randomized clinical trials, interim monitoring allows early stopping; however, there are few methods for principled interim analysis in SMARTs. Because SMARTs involve multiple stages of treatment, a key challenge is that not all enrolled participants will have progressed through all treatment stages at the time of an interim analysis. Wu et al. (2021) propose basing interim analyses on an estimator for the mean outcome under a given regime that uses data only from participants who have completed all treatment stages. We propose an estimator for the mean outcome under a given regime that gains efficiency by using partial information from enrolled participants regardless of their progression through treatment stages. Using the asymptotic distribution of this estimator, we derive associated Pocock and O'Brien-Fleming testing procedures for early stopping. In simulation experiments, the estimator controls type I error and achieves nominal power while reducing expected sample size relative to the method of Wu et al. (2021). We present an illustrative application of the proposed estimator based on a recent SMART evaluating behavioral pain interventions for breast cancer patients.

翻译：序贯多组随机试验（SMART）是生成多阶段治疗方案评估数据的金标准试验设计。与传统（单阶段）随机临床试验类似，中期监测允许提前终止试验，但目前针对SMART的原则性中期分析方法较少。由于SMART涉及多阶段治疗，一个关键挑战是在中期分析时，并非所有入组参与者都已完成所有治疗阶段。Wu等人（2021年）提出基于特定方案下仅使用已完成所有治疗阶段参与者数据的结果均值估计量进行中期分析。本文提出一种新的特定方案下结果均值估计量，该估计量通过利用所有入组参与者（无论其处于治疗各阶段的哪个进度）的部分信息来提高效率。基于该估计量的渐近分布，我们推导出相应的Pocock和O'Brien-Fleming检验流程用于提前终止试验。模拟实验表明，相较于Wu等人（2021）的方法，该估计量在控制第一类错误率并达到名义检验效能的同时，减少了期望样本量。我们基于一项近期针对乳腺癌患者疼痛行为干预评估的SMART研究，展示了所提估计量的实际应用案例。