Compare adaptive interventions in a SMART with a continuous, longitudinal outcome (linear mixed modeling)

About This Code

This code is used to estimate and compare the mean change in a longitudinal outcome for the adaptive interventions embedded in a SMART using a marginal linear mixed modeling approach.

How can a behavioral scientist use this code?

Behavioral intervention scientists can use this code to make inferences about the relative causal effects of one adaptive intevention versus another using data from a SMART with a longitudinal outcome.

What method does this code implement?

This code implements a pseudo maximum likelihood estimation (MLE) approach. A pseudo MLE is necessary because we are targeting a marginal likelihood (as opposed to targeting the likelihood that gave rise to the data); and therefore weights are used to define the likelihood function. In a SMART, the weights (which are potentially impacted by treatment) are known, by design.

Access Links

View Code on GitHub

Upcoming Events

View All Events

Introduction to MAISYs

Prevention Science and Methodology Group Virtual Grand Rounds
January 24, 2023

Introduction to MAISYs

University of Florida Health Outcomes and Biomedical Informatics Grand Rounds
February 13, 2023
Training Institute

Getting SMART

Comprehensive Program for Adaptive Interventions Training in Education (CATIE)
March 14, 2023
View All Events


Join the d3center Mailing List

Keep up to date with the latest news, events, software releases, learning modules, and resources from the d3center.