Adaptive interventions (AIs) are increasingly recognized as essential tools for personalized care across various fields, including medicine, psychology, education, and public health. These interventions aim to improve outcomes by dynamically tailoring treatment strategies to an individual’s changing needs over time. However, clinicians and researchers face critical questions when designing effective adaptive interventions: which treatment to offer, when to offer it, and for whom it is best suited at each decision stage. Sequential Multiple Assignment Randomized Trials (SMART) provide a rigorous framework to address these questions, allowing for the empirical optimization of adaptive interventions to enhance precision and effectiveness. This talk will explore the key components of adaptive interventions, highlighting design principles and common applications. Attendees will learn how SMARTs support the optimization of adaptive interventions and examine common primary and secondary aims along with their associated statistical methodologies. We will present practical examples to illustrate the real-world application of SMART designs, offering a comprehensive perspective on their potential to advance personalized intervention science.

This presentation will be held in 2036 Palmer Commons. There will also be a remote viewing option via Zoom.