Existing statistical methods for the analysis of micro-randomized trials (MRTs) are designed to estimate causal excursion effects using data from a single MRT. In practice, however, researchers can often find previous MRTs that employ similar interventions. In this paper, we develop data integration methods that capitalize on this additional information, leading to statistical efficiency gains. To further increase efficiency, we demonstrate how to combine these approaches according to a generalization of multivariate precision weighting that allows for correlation between estimates, and we show that the resulting meta-estimator possesses an asymptotic optimality property. We illustrate our methods in simulation and in a case study involving two MRTs in the area of smoking cessation.

翻译：现有的微随机试验（MRT）统计方法旨在利用单次MRT数据估计因果瞬时效应。然而在实践中，研究者常能发现采用相似干预措施的既往MRT研究。本文开发了利用此类额外信息的数据整合方法，从而提升统计效率。为进一步提高效率，我们展示了如何根据允许估计量间存在相关性的多变量精度加权推广方法，将这些方法进行组合；并证明所得元估计量具有渐近最优性。我们通过模拟实验和一项涉及两项戒烟领域MRT的案例研究验证了所提方法的有效性。