Researchers and policymakers can conceptualize many health policies as adaptive interventions, which incorporate adjustments of recommended action(s) at each decision point based on previous outcomes or actions. Health policy interventions often involve intervening at a system-level (e.g., a primary care clinic) with the intent to modify behavior of individuals within the system (e.g., doctors in a clinic). Policy scientists (e.g., implementation scientists) can use clustered, sequential, multiple assignment, randomized trials (SMART) to compare such “multilevel adaptive interventions” on a nested, end-of-study outcome. However, existing methods are not suitable when the primary outcome in a clustered SMART is nested and longitudinal; e.g., repeated outcome measures nested within each clinician and clinicians nested within sequentially-randomized clinics. In this manuscript, we propose a three-level marginal mean modeling and estimation approach for comparing multilevel adaptive interventions in a clustered SMART. This methodology accommodates both the cross-temporal within-unit correlation in the longitudinal outcome and the inter-unit correlation within each cluster. We illustrate our methods using data from two clustered, health-policy SMARTs: the first aims to improve guideline concordant opioid prescribing in non-cancer primary care clinics in Wisconsin and Michigan; and the second aims to improve the adoption of evidence-based mental health treatments in high schools across Michigan.

Graduate Student Research Assistant

d3center

Institute for Social Research