Sequential, multiple assignment randomized trials (SMARTs), which assist in the optimization of adaptive interventions, are growing in popularity in education and behavioral sciences. This is unsurprising, as adaptive interventions reflect the sequential, tailored nature of learning in a classroom or school. Nonetheless, as is true elsewhere in education research, observed effect sizes in education-based SMARTs are frequently small. As a consequence, statistical efficiency is of paramount importance in their analysis. The contributions of this manuscript are two-fold. First, we provide an overview of adaptive interventions and SMART designs for researchers in education science. Second, we propose four techniques that have the potential to improve statistical efficiency in the analysis of SMARTs. We demonstrate the benefits of these techniques in SMART settings both through the analysis of a SMART designed to optimize an adaptive intervention for increasing cognitive behavioral therapy delivery in school settings and through a comprehensive simulation study. Each of the proposed techniques is easily implementable, either with over-the-counter statistical software or through R code provided in an online supplement.

翻译：序列式多分配随机试验（SMART）旨在优化自适应干预，在教育与行为科学领域日益流行。这不足为奇，因为自适应干预反映了课堂或学校学习中序列化、个性化的特点。然而，正如教育研究的其他领域一样，基于SMART的观察效应量通常较小。因此，统计效率在其分析中至关重要。本文的贡献有两方面：首先，我们为教育科学领域的研究人员提供了自适应干预和SMART设计的概述；其次，我们提出了四种有望提升SMART分析统计效率的技术。通过分析一项旨在优化学校环境中认知行为治疗实施自适应干预的SMART研究，并结合一项综合性模拟研究，我们展示了这些技术在SMART情境中的优势。上述每种技术都易于实现，既可直接使用市售统计软件，也可通过在线补充材料提供的R代码完成。