University of Wisconsin-Madison
Virtual – Register online HERE.
Danny Almirall – Associate Professor/Co-Director of Data Science for Dynamic Intervention Decision-making Center (d3c) Department of Statistics, College of Literature, Sciences, and the Arts Survey Research Center, Institute for Social Research University of Michigan.
Please join us August 12th from 12:00 – 1:00 PM via Zoom for this month’s guest speaker, Daniel Almirall, a statistician and effectiveness-implementation methodologist who develops tools to form evidence-based adaptive interventions.
Evidence-based treatments often fail to be implemented or sustained due to barriers at multiple levels. A growing cadre of implementation strategies—theory-based techniques used to support uptake of services and interventions—can help mitigate challenges at these levels, but significant heterogeneity exists in how practitioners and clinics respond to different strategies, and it is impractical to provide all (or even most) of these strategies to all levels at all times. A Multi-level Adaptive Implementation Strategy (MAISY) offers an approach to precision implementation of evidence-based practices in real-world settings. MAISY guides implementers on how best to adapt (e.g., augment, intensify) implementation strategies based on context and needs at multiple levels (e.g., clinic, practitioner). In this presentation, we identify common types of scientific questions concerning the optimization of MAISYs. We also describe different types of randomized trial designs that can be used to answer such questions. This includes Multilevel Implementation Sequential Multiple Assignment Randomized Trials (MI-SMARTs). And we develop a set of guiding principles concerning when to use an MI-SMART vs a different type of trial design.