Topic: Building Effective Just-in-Time Adaptive Interventions Using Micro-Randomized Trial Designs

Presenters: Susan Murphy and Daniel Almirall

Date: RESCHEDULED to October 25-26, 2021

Venue: TBD

Workshop Information

A just-in-time adaptive intervention (JITAI) is an emerging mobile health intervention design aiming to provide support “just-in-time”, namely, whenever and wherever support is needed. A JITAI does this via adaptation. The JITAI employs wearable sensors and other approaches to data collection to monitor ongoing information on the dynamics of an individual’s emotional, social, physical and contextual states. The adaptation occurs when this information is used to individualize the type and delivery timing of support. The adaptation in a JITAI is intended to ensure that the right type of support is provided whenever the person is (a) vulnerable and/or in a state of opportunity, and (b) receptive, namely, able and willing to receive, process and utilize the support provided.

We will introduce the micro-randomized trial (MRT), a new trial design useful for addressing scientific questions concerning the construction of highly-effective JITAIs. We will provide an introduction to JITAIs, as well as examples of key scientific questions that need to be addressed in the development of these interventions. We will discuss MRTs and how they can be used to answer these scientific questions. Useful primary aim data analysis methods for MRTs will also be discussed.

The emphasis of this workshop is on JITAI and MRT design considerations and applications. Most of Day 1 and some of Day 2 focus on this. On Day 2, much of the time will be allotted to understanding primary aims in an MRT and conducting associated primary aim analyses.


The prerequisites for this workshop are (1) familiarity with the basic principles of experimental (e.g., randomized trial) design, and (2) graduate-level statistics training for the behavioral, management, social or health sciences up through linear regression (usually two semesters of course work). Basic familiarity with the R programming language is necessary for participation in the computer exercises.


Participants will be provided with a hard copy of all lecture notes, select computer exercises, and output. Three different formats will be used. First, all materials will be presented following the standard didactic format with a slideshow. Second, there will be practice exercises (i.e., practicums) designed to help participants connect the material with their own research area. These practicums are aimed at helping investigators learn how to implement an MRT and helping to prepare participants to write a grant proposal that uses an MRT design to build a JITAI. Third, there will be computer exercises using R on Day 2 of the workshop. Computer code and simulated data examples will be supplied by the instructors. The computer exercises will help investigators learn how to conduct and interpret the results of typical primary and secondary analyses. Throughout the workshop, ample time will be set aside for Q&A and discussion about how the concepts learned in class can be applied in participants’ research.

Computer Requirements

Participants are strongly encouraged to bring a laptop so that they can participate in the computer exercises. To conduct analyses at the workshop, the latest version of R must be installed on your laptop prior to arrival.

We cannot provide IT support (e.g., R installation, troubleshoot errors running R) at the workshop. However, we expect that even if you do experience some difficulty with R (or other software trouble with your laptop), you will still be able to appreciate and learn from the computer portions of the workshop.

Topics Covered

How to Attend

Enrollment is limited to 40 participants to maintain an informal atmosphere and to encourage interaction between and among the presenters and participants. We will give priority to individuals who are involved in substance use prevention and treatment research or HIV research, who have the appropriate statistical background to get the most out of the Institute, and for whom the topic is directly and immediately relevant to their current work. We also aim to maximize geographic and minority representation.

Applications to the 2020 Summer Institute were due by 5 p.m. Eastern Time, Monday, March 2, 2020. Applicants were notified about decisions in early April 2020.

Once accepted, participants will be emailed instructions about how to register. The registration fee of $395 for the two-day Institute will cover all instruction, program materials, and a reception the first evening of the Institute. A block of rooms will be available for lodging.

Participants are encouraged to bring their own laptop computers for conducting exercises.

Review our refund, access, and cancellation policy.

The application window for the 2020 Summer Institute is closed.


Susan Murphy, Ph.D.


Susan Murphy is Professor of Statistics, Computer Science and Radcliffe Alumnae Professor, Harvard University.

Dr. Murphy’s lab develops data analysis methods and experimental designs to improve real time sequential decision-making in mobile health. In particular, her lab develops algorithms, deployed on wearable devices, to deliver and continually optimize individually tailored treatments. She developed the micro-randomized trial for use in constructing mobile health interventions; this trial design is in use across a broad range of health related areas. In these trials each participant can be randomized or re-randomized 100’s of times. Browse a list of micro-randomized trials that are completed or are in the field.

Dr. Murphy is a member of the National Academy of Sciences and of the National Academy of Medicine, both of the U.S. National Academies. In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making.

Daniel Almirall, Ph.D.


Daniel Almirall is Research Associate Professor at the University of Michigan’s Survey Research Center.

Dr. 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.




Funding for this conference was made possible by award number R13 DA020334 from the National Institute on Drug Abuse. The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official views and/or policies of the Department of Health and Human Services; nor does mention of trade names, commercial practices, or organizations imply endorsement by the U.S. Government.


  • 2019 – Variability in Intensive Longitudinal Data: Mixed-Effects Location Scale Modeling by Donald Hedeker
  • 2018 – Analysis of Ecological Momentary Assessment Data by Stephanie T. Lanza and Michael Russell
  • 2017 – Statistical Power Analysis for Intensive Longitudinal Studies by Jean-Philippe Laurenceau and Niall Bolger
  • 2016 – Ecological Momentary Assessment (EMA): Investigating Biopsychosocial Processes in Context by Joshua Smyth, Kristin Heron, and Michael Russell
  • 2015 – An Introduction to Time-Varying Effect Modeling by Stephanie T. Lanza and Sara Vasilenko
  • 2014 – Experimental Design and Analysis Methods for Developing Adaptive Interventions: Getting SMART by Daniel Almirall and Inbal Nahum-Shani
  • 2013 – Introduction to Latent Class Analysis by Stephanie Lanza and Bethany Bray
  • 2012 – Causal Inference by Donna Coffman
  • 2011 – The Multiphase Optimization Strategy (MOST) by Linda Collins
  • 2010 – Analysis of Longitudinal Dyadic Data by Niall Bolger and Jean-Philippe Laurenceau
  • 2009 – Latent Class and Latent Transition Analysis by Linda Collins and Stephanie Lanza
  • 2008 – Statistical Mediation Analysis by David MacKinnon
  • 2007 – Mixed Models and Practical Tools for Causal Inference by Donald Hedeker and Joseph Schafer
  • 2006 – Causal Inference by Christopher Winship and Felix Elwert
  • 2005 – Survival Analysis by Paul Allison
  • 2004 – Analyzing Developmental Trajectories by Daniel Nagin
  • 2003 – Modeling Change and Event Occurrence by Judith Singer and John Willett
  • 2002 – Missing Data by Joseph Schafer
  • 2001 – Longitudinal Modeling with MPlus by Bengt Muthén and Linda Muthén
  • 2000 – Integrating Design and Analysis and Mixed-Effect Models by Richard Campbell, Paras Mehta, and Donald Hedeker
  • 1999 – Structural Equation Modeling by John McArdle
  • 1998 – Categorical Data Analysis by David Rindskopf and Linda Collins
  • 1997 – Hierarchical Linear Models and Missing Data Analysis by Stephen Raudenbush and Joseph Schafer
  • 1996 – Analysis of Stage Sequential Development by Linda Collins, Peter Molenaar, and Han van der Maas