“Cluster-level adaptive interventions and associated clustered sequentially randomized trial designs”
Thursday, January 14th, 2021
11:00AM–12:15 PM via Zoom
Register in advance: https://go.uic.edu/CDIS_Colloquium_Jan21
CDIS Colloquium Series
Co-Sponsored by CDIS, Richard Weber, and the Center for Clinical and Translational Science
Daniel Almirall, PhD
Co-Director, Data Science for Dynamic Intervention Decision-making Laboratory (d3lab)
Survey Research Center, Institute for Social Research
Department of Statistics, College of Literature Sciences and the Arts
University of Michigan
Speaker Bio: Daniel Almirall is a statistician who develops methods to form evidence-based adaptive interventions. Adaptive interventions can be used to inform individualized intervention guidelines for the on-going management of chronic illnesses or disorders such as drug abuse, depression, anxiety, autism, obesity, or HIV/AIDS. More recently, Dr. Almirall has been interested in methods to form related adaptive implementation interventions and just-in-time-adaptive interventions in mobile health. His work includes the development of approaches related to the design, execution, and analysis of sequential multiple assignment randomized trials (SMARTs) and micro-randomized trials (MRTs). He is particularly interested in applications in child and adolescent mental health research.
Presentation Abstract: A cluster-level adaptive intervention is a pre-specified, replicable sequence of decision rules that guides how to make ongoing critical intervention decisions at the cluster level, such as at the level of the school or clinic. Clustered, sequential, multiple-assignment randomized trials (SMART) are experimental designs in which sequential randomization occurs at the cluster level, with outcomes at the individual-level. Clustered SMARTs are used to inform the empirical development of cluster-level adaptive interventions that optimize outcomes for the individuals that make up the cluster. In this manuscript, we develop a novel data analysis method to construct empirically-supported adaptive interventions using data from a clustered SMART. This includes the development of methods for making statistical inference concerning the usefulness of candidate tailoring variables at the cluster-level. To illustrate our methods, we utilize Adaptive School-based Implementation of CBT (ASIC) study. ASIC is a cluster randomized SMART of schools across Michigan to develop an optimal school-level adaptive intervention for improving the adoption of cognitive behavioral therapy at schools.