AI 中文总结
该研究针对含分类处理与二元近端结局的微随机试验，定义因果偏移效应，提出EMEE-catA估计量与样本量公式，经模拟验证稳健性并给出实践指导，以Drink Less MRT数据示例说明方法。
AI 中文摘要
微随机试验（MRTs）为评估移动健康（mHealth）干预措施的边际效应与调节效应提供了框架。在诸多应用场景中，处理方式呈现为具有多个水平的分类变量，例如不同的消息内容或推送策略。移动健康研究中许多具有科学意义的纵向结局为二元变量，例如受试者是否打开应用、与内容互动，或在处理被随机分配的决策点后完成目标行为。本文聚焦于含分类处理与二元近端结局的微随机试验，定义了因果偏移效应，提出了名为EMEE-catA的估计量，并推导了用于比较分类处理水平的样本量公式，该公式可控制I类错误率并在工作假设下保证统计功效。我们开展了广泛的模拟研究以评估所提样本量公式的操作特征，包括对这些假设违背情况的稳健性。我们还为实施所提方法提供了实用指导，以确保真实世界微随机试验中具备足够的统计功效。这些方法通过“少喝一点”微随机试验（Drink Less MRT）的数据得到了示例说明。
英文摘要
Micro-randomized trials (MRTs) provide a framework for evaluating the marginal and moderated effects of mobile health (mHealth) interventions. In many applications, treatments take the form of categorical variables with multiple levels, such as different message contents or delivery strategies. Many scientifically meaningful longitudinal outcomes in mHealth studies are binary, such as whether a participant opens an app, engages with content, or completes a target behavior following a decision point at which treatment is randomized. This paper focuses on MRTs with categorical treatments and binary proximal outcomes. We define the causal excursion effect, propose an estimator called EMEE-catA, and derive a sample size formula for comparing categorical treatment levels that controls the type I error rate and guarantees power under working assumptions. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed sample size formula, including robustness to violations of these assumptions. We further provide practical guidance for implementing the proposed approach to ensure adequate power in real-world MRTs. The methods are illustrated using data from the Drink Less MRT.
Comments89 pages, 13 figures, 5 tables. Supplementary material is included in the same file, starting on page 43
