Small sample, sequential, multiple assignment, randomized trials (snSMARTs) are multistage trials with the overall goal of determining the best treatment after a fixed amount of time. In snSMART trials, patients are first randomized to one of three treatments and a binary (e.g. response/nonresponse) outcome is measured at the end of the first stage. Responders to first stage treatment continue their treatment. Nonresponders to first stage treatment are rerandomized to one of the remaining treatments. The same binary outcome is measured at the end of the first and second stages, and data from both stages are pooled together to find the best first stage treatment. However, in many settings the primary endpoint may be continuous, and dichotomizing this continuous variable may reduce statistical efficiency. In this article, we extend the snSMART design and methods to allow for continuous outcomes. Instead of requiring a binary outcome at the first stage for rerandomization, the probability of staying on the same treatment or switching treatment is a function of the first stage outcome. Rerandomization based on a mapping function of a continuous outcome allows for snSMART designs without requiring a binary outcome. We perform simulation studies to compare the proposed design with continuous outcomes to standard snSMART designs with binary outcomes. The proposed design results in more efficient treatment effect estimates and similar outcomes for trial patients.

中文翻译：

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在小样本、顺序、多重分配、具有连续结果的随机试验中使用映射函数的设计和分析注意事项

小样本、顺序、多重分配、随机试验 (snSMART) 是多阶段试验，总体目标是在固定时间后确定最佳治疗方案。在 snSMART 试验中，患者首先被随机分配到三种治疗中的一种，并在第一阶段结束时测量二元（例如反应/无反应）结果。第一阶段治疗的响应者继续他们的治疗。对第一阶段治疗无反应者被重新随机分配至其余治疗之一。在第一阶段和第二阶段结束时测量相同的二元结果，并将两个阶段的数据汇集在一起​​，以找到最佳的第一阶段治疗。然而，在许多情况下，主要终点可能是连续的，将这个连续变量二分法可能会降低统计效率。在本文中，我们扩展了 snSMART 设计和方法，以实现持续的结果。不需要在第一阶段再随机化的二元结果，保持相同治疗或转换治疗的概率是第一阶段结果的函数。基于连续结果的映射函数的重新随机化允许 snSMART 设计而不需要二元结果。我们进行模拟研究，以将具有连续结果的提议设计与具有二元结果的标准 snSMART 设计进行比较。所提出的设计为试验患者带来了更有效的治疗效果估计和类似的结果。保持相同治疗或转换治疗的概率是第一阶段结果的函数。基于连续结果的映射函数的重新随机化允许 snSMART 设计而不需要二元结果。我们进行模拟研究，以将具有连续结果的提议设计与具有二元结果的标准 snSMART 设计进行比较。所提出的设计为试验患者带来了更有效的治疗效果估计和类似的结果。保持相同治疗或转换治疗的概率是第一阶段结果的函数。基于连续结果的映射函数的重新随机化允许 snSMART 设计而不需要二元结果。我们进行模拟研究，以将具有连续结果的提议设计与具有二元结果的标准 snSMART 设计进行比较。所提出的设计为试验患者带来了更有效的治疗效果估计和类似的结果。