Guest Lecture: Multilevel Adaptive Implementation Strategies (MAISYs)

Guest lecture by Prof. Daniel Almirall

Wednesday, 7th May 2025, 12:30–1:30 pm
RAA-G-15, Rämistrasse 59, 8001 Zurich link to location

Evidence-based practices often fail to be implemented or sustained due to barriers at multiple levels of an organization (e.g., system-level, practitioner-level). A growing cadre of implementation strategies can help mitigate challenges at these multiple levels, but significant heterogeneity exists in whether, and to what extent, organizations—and the practitioners who deliver treatment within them—respond to different strategies. However, it is impractical to provide all (or even most) of these strategies to all levels, always. This suggests the need for an approach that sequences and adapts the provision of implementation strategies to the changing context and needs of practitioners within the multiple levels of an organization. A multi-level adaptive implementation strategy (MAISY) offers a replicable, approach to precision implementation that guides implementers in how best to adapt and re-adapt (e.g., augment, intensify, switch) implementation strategies based on the changing context and changing needs at multiple levels.

Attendees will:

• Learn about Multilevel Adaptive Implementation Strategies (MAISYs) and basic MAISY design principles.
• Learn about a variety of novel scientific questions whose answers can be used to construct an optimized MAISY.
•  Learn about different types of optimization trial (experimental) designs, e.g. SMART designs (Sequential, Multiple Assignment, Randomized Trials), and how to match the right trial design with the scientific questions of interest
• Begin to understand the difference between the optimization and evaluation of MAISYs .

Prof. Daniel Almirall is a statistician who develops and applies methods to form evidence-based adaptive interventions, including multilevel adaptive interventions and just-in-time adaptive interventions. Adaptive interventions are used to guide ongoing intervention decision-making, such as clinical decisions for the on-going management of chronic illnesses such as drug abuse, depression, anxiety, autism, obesity, or HIV/AIDS. His methodological research includes the development of novel data collection designs, such as sequential multiple assignment randomized trials (SMART), clustered SMARTs, multilevel SMARTs, micro-randomized trials (MRT) and associated analytic methods.

You can register for the guest lecture here – participation is free of charge.