Adaptive interventions (AIs) are increasingly popular in the behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, multiple assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable only for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This calls for noninferiority/equivalence testing methods, because AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or noninferior to those produced by a more costly/burdensome standard of care. Here, we develop data-analytic methods and sample-size formulas for SMARTs testing the noninferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online resources for sample size planning are provided. A simulated data analysis shows how to test noninferiority and equivalence hypotheses with SMART data. For illustration, we use an example from a SMART in the area of health psychology aiming to develop an AI for promoting weight loss among overweight/obese adults. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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序贯、多重分配、随机试验 (SMART) 中的非劣效性和等效性检验。


适应性干预（AI）在行为科学中越来越受欢迎。人工智能是一系列决策规则，指定应为谁以及在什么条件下提供不同的干预选项，以便满足个人随着时间的推移不断变化的需求。序贯、多重分配、随机试验 (SMART) 是一种新颖的试验设计，旨在帮助凭经验构建有效的 AI。 SMART 中的顺序随机化通常会产生多个人工智能，这些人工智能被设计嵌入到试验中。许多 SMART 都是出于与此类嵌入式人工智能比较相关的科学问题。 SMART 的现有数据分析方法和样本量规划资源仅适用于优越性测试，即测试一个嵌入式人工智能平均是否比另一个嵌入式人工智能产生更好的主要结果。这就需要非劣效性/等效性测试方法，因为人工智能的动机通常是需要以成本更低或负担更少的方式提供支持/护理，同时仍然产生与成本更高/负担更重的标准所产生的效益相当或不逊色的效益的照顾。在这里，我们为 SMART 开发数据分析方法和样本量公式，测试一种人工智能相对于另一种人工智能的非劣效性或等效性。通过支持模拟讨论了样本大小和功效考虑因素，并提供了用于样本大小规划的在线资源。模拟数据分析展示了如何使用 SMART 数据检验非劣效性和等价性假设。为了说明这一点，我们使用健康心理学领域的 SMART 示例，旨在开发人工智能来促进超重/肥胖成年人减肥。 （PsycINFO 数据库记录 (c) 2019 APA，保留所有权利）。