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2022 Aug DRISS - MAISY: Multilevel Adaptive Implementation Strategies & Optimization Trial Designs
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Dissemination Research and Implementation Science Seminar (DRISS) | University of Wisconsin - Madison | August 2022
DRISS is an interactive, monthly lunchtime seminar (via Zoom) focused on learning about the growing science of dissemination and implementation research and practice!
Seminar Description:
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.
Speaker:
Daniel Almirall (he/his/él) Associate Professor Co-Director, 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
Daniel Almirall is a statistician and effectiveness-implementation methodologist who develops tools to form evidence-based adaptive interventions. Adaptive interventions are used to guide individualized intervention decision-making by clinicians for the on-going management of chronic illnesses or disorders such as drug abuse, depression, anxiety, autism, obesity, or HIV/AIDS. Mr. Almirall is an expert in methods related to the design, execution, and analysis of sequential, multiple-assignment, randomized trials (SMARTs), which are used to construct optimized AIs. More recently, Mr. Almirall has been developing methods to inform adaptive implementation strategies and multilevel adaptive implementation interventions (MAISYs). These are multicomponent implementation strategies used to guide ongoing decision-making by implementers, such as which strategy to offer which organization, at what level of organization, when and based on which measures. This includes the development of design and analysis methods for Clustered SMARTs and Multilevel Implementation SMARTs. He is particularly interested in applications in mental health, substance use and education. He is a principal investigator of the NIDA-funded P50 MAPS Center (Methodologies for Adapting and Personalizing Prevention, Treatment, and Recovery Services for SUD and HIV), which is housed at the Data Science for Intervention Decision-making Center (d3c) at the University of Michigan.
DRISS is an interactive, monthly lunchtime seminar (via Zoom) focused on learning about the growing science of dissemination and implementation research and practice!
Seminar Description:
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.
Speaker:
Daniel Almirall (he/his/él) Associate Professor Co-Director, 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
Daniel Almirall is a statistician and effectiveness-implementation methodologist who develops tools to form evidence-based adaptive interventions. Adaptive interventions are used to guide individualized intervention decision-making by clinicians for the on-going management of chronic illnesses or disorders such as drug abuse, depression, anxiety, autism, obesity, or HIV/AIDS. Mr. Almirall is an expert in methods related to the design, execution, and analysis of sequential, multiple-assignment, randomized trials (SMARTs), which are used to construct optimized AIs. More recently, Mr. Almirall has been developing methods to inform adaptive implementation strategies and multilevel adaptive implementation interventions (MAISYs). These are multicomponent implementation strategies used to guide ongoing decision-making by implementers, such as which strategy to offer which organization, at what level of organization, when and based on which measures. This includes the development of design and analysis methods for Clustered SMARTs and Multilevel Implementation SMARTs. He is particularly interested in applications in mental health, substance use and education. He is a principal investigator of the NIDA-funded P50 MAPS Center (Methodologies for Adapting and Personalizing Prevention, Treatment, and Recovery Services for SUD and HIV), which is housed at the Data Science for Intervention Decision-making Center (d3c) at the University of Michigan.
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