B1325
Title: Improving the efficiency of time-varying causal effect moderation analysis in mobile health
Authors: Walter Dempsey - University of Michigan (United States) [presenting]
Abstract: Twin revolutions in wearable technologies and smartphone-delivered digital health interventions have significantly expanded the accessibility and uptake of mobile health (mHealth) interventions. Sequentially randomized experiments called micro-randomized trials (MRTs) have grown in popularity as a means to empirically evaluate the effectiveness of mHealth intervention components. MRTs have motivated a new class of causal estimands, termed causal excursion effects. We revisit the estimation of causal excursion effects and present two new tools for improving efficiency. First, we will present a method to improve efficiency by including auxiliary variables. This method extends the covariate-adjustment RCT literature to the time-varying setting. Second, we will consider a meta-learner perspective, where any supervised learning algorithm can be used to assist in the estimation of the causal excursion effect. Theoretical comparisons accompanied by extensive simulation experiments demonstrate the relative efficiency gains. The practical utility of the proposed methods is demonstrated by analyzing data from a multi-institution cohort of first-year medical residents in the United States.
