Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect Heterogeneity

• Activity Number: 165 - SLDS CSpeed 2
• Type: Contributed
• Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Jieru Hera Shi* and Zhenke Wu and Walter Dempsey
• Companies: University of Michigan, Ann Arbor and University of Michigan, Ann Arbor and University of Michigan
• Keywords: Causal Inference; Clustered Data; Just-In-Time Adaptive Interventions; Micro-randomized Trials; Mobile Health; Moderation Effect

Abstract

The micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points. The MRT context has motivated a new class of causal estimands, termed "causal excursion effects", for which inference can be made by a weighted, centered least squares approach (Boruvka et al., 2017). Existing methods assume between-subject independence and non-interference. Deviations from these assumptions often occur which, if unaccounted for, may result in bias and overconfident variance estimates. In this paper, causal excursion effects are considered under potential cluster-level correlation and interference and when the treatment effect of interest depends on cluster-level moderators. The utility of our proposed methods is shown by analyzing data from a multi-institution cohort of first year medical residents in the United States. The approach paves the way for the construction of mHealth interventions that account for observed social network information.