论文标题
使用部分信息对顺序多个分配随机试验进行临时监视
Interim Monitoring of Sequential Multiple Assignment Randomized Trials Using Partial Information
论文作者
论文摘要
顺序多重分配随机试验（SMART）是黄金标准试验设计，旨在生成数据以评估多阶段治疗方案。与常规（单级）随机临床试验一样，临时监测允许尽早停止。但是，智能中有原则的临时分析方法很少。由于智能疗法涉及多个治疗阶段，因此一个关键的挑战是，在临时分析时，并非所有注册的参与者都会在所有治疗阶段取得进展。 Wu等。 （2021）在给定制度下仅使用仅完成所有治疗阶段的参与者的数据，对估计量进行了临时分析。我们提出了一个在给定制度下的平均结果的估计量，该估计量通过使用注册参与者的部分信息来提高效率，无论他们通过治疗阶段的进展如何。使用该估计量的渐近分布，我们得出了相关的Pocock和O'Brien-Fleming测试程序，以提早停止。在仿真实验中，估计器控制I型误差并达到标称功率，同时相对于Wu等人的方法降低了预期样本量。 （2021）。我们基于最近的智能评估乳腺癌患者的行为疼痛干预措施，对拟议估计量进行了说明性的应用。
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.
