We hope that everything is going well for everyone. This is a reminder that the next Workshop on Quantitative Methods in Education, Health and the Social Sciences (QMeHSS) will be this Friday, May 1st, from 11:00-12:30pm. The workshop will be led by Dr. Daniel Almirall from the University of Michigan. As with previous workshops, it will be held in NORC conference room 344. NORC is located at 1155 E. 60th Street. We look forward to seeing you there.
On the Comparison of Adaptive Interventions Using Repeated Measures Data from a SMART: Applications in Child and Adolescent Mental Health
Daniel Almirall, PhD
Research Assistant Professor, Survey Research Center, Institute for Social Research, University of Michigan
An adaptive intervention is a sequence of individually-tailored decisions rules that specify whether, how or when to alter the intensity, type or dosage of treatment at critical decision points in the course of care. Adaptive interventions (often referred to as dynamic treatment regimens) provide clinicians with a guide for how to adapt and re-adapt treatment over time, in response to the changing needs or circumstances of the individual. They can be used as a guide to individualized (personalized) clinical practice. Sequential, multiple-assignment randomized trials (SMART) were developed explicitly for the purpose of constructing high-quality adaptive interventions using experimental design principles. Most SMARTs have a number of adaptive interventions embedded within them. A common scientific aim is the comparison of study outcomes between these adaptive interventions.
In this talk, we review adaptive interventions and SMART, and we present recent methodological work on how to compare the adaptive interventions embedded in a SMART using a repeated measures outcome. Specifically, we discuss longitudinal modeling considerations that are unique to SMARTs; we describe an easy-to-use, weighted-and-replicated (WR) regression estimator for analyzing the repeated measures; and we discuss simple ways to improve the statistical efficiency of the WR estimator. The methodology is illustrated using data from two SMARTs: the first is aimed at developing an adaptive intervention to improve spoken communication in minimally verbal children with autism, the second focuses on developing a school-based adaptive intervention for children with ADHD. We also discuss future directions, including the use of novel, cluster-randomized SMARTs in organizational research.