Abstract: An adaptive intervention is a sequence of decision rules that guide the provision of intervention at critical decision points based on the evolving needs of individuals. In many health, education and policy settings, adaptive interventions target clusters of individuals (e.g., clinics or schools), with the intent of improving outcomes at the level of individuals. These are called clustered adaptive interventions (cAIs). Clustered, sequential, multiple-assignment, randomized trials (cSMART) can be used to form high-quality clustered adaptive interventions. This non-technical talk introduces clustered adaptive interventions and cSMARTs, and presents current and past research concerning the design and analysis of cSMARTs. This includes recent work with University of Michigan students on finite-sample adjustment methods for the marginal mean comparison of cAIs (with W. Pan), an extension of moderator analyses using ideas from Q-learning (with Y. Song), and a three-level analysis method (with G. Durham)—i.e., repeated measures nested within individuals, nested within clusters. It also includes a new cSMART design for the construction of event-triggered cAIs (with M. Ferlic). Time-permitting, the talk ends by describing current challenges in the design and analysis of multilevel SMARTs, a special type of clustered SMART, whereby nested groups of individuals are randomized sequentially (e.g., a clinic-level randomization followed by a doctor-within-clinic-level randomization) leading to interesting questions concerning causal spillover. Throughout, I draw on multiple examples from implementation science, a relatively new area that concerns the optimization and evaluation of clustered interventions to improve the adoption of evidence-based practices.


https://d3c.isr.umich.edu/person/daniel-almirall/