Contextual sensing and delivery of digital interventions to improve health outcomes have gained significant traction in behavioral and psychiatric studies. Micro-randomized trials (MRTs) are a common experimental design for obtaining data-driven evidence on the effectiveness of digital interventions where each individual is repeatedly randomized to receive treatments over numerous time points. Throughout the study, individual characteristics and contextual factors around randomization are collected, with some prespecified as moderators for assessing time-varying causal effect moderation. However, many additional measurements beyond these moderators often go underutilized. Some of these may influence treatment randomization or known to strongly moderate the treatment effect. Incorporating such auxiliary information into the estimation procedure can reduce chance imbalances and improve asymptotic estimation efficiency. In this work, we propose a method to adjust for auxiliary variables in consistently estimating time-varying intervention effects. The approach can also be extended to include post-treatment auxiliary variables when evaluating lagged treatment effects. Under specific conditions, local efficiency gains are guaranteed. We demonstrate the method's utility through simulation studies and an analysis of data from the Intern Health Study (NeCamp et al., 2020).

翻译：在行为与精神病学研究中，通过情境感知和数字干预传递以改善健康结果的方法已获得显著关注。微随机试验是一种常见的实验设计，用于获取关于数字干预有效性的数据驱动证据，其中每个个体在多个时间点上被重复随机分配接受治疗。在整个研究过程中，个体特征和随机化时的情境因素被收集，其中一些预先指定为调节变量，用于评估时变因果效应的调节作用。然而，超出这些调节变量的许多额外测量往往未得到充分利用。其中一些变量可能影响治疗随机化，或已知对治疗效果有较强的调节作用。将此类辅助信息纳入估计过程可以减少偶然性不平衡并提高渐近估计效率。在本研究中，我们提出了一种在一致估计时变干预效应时调整辅助变量的方法。该方法还可扩展至包含治疗后辅助变量，以评估滞后治疗效果。在特定条件下，该方法可保证局部效率提升。我们通过模拟研究和对Intern Health Study数据的分析，证明了该方法的实用性。