Topic: Analysis of Ecological Momentary Assessment Data Using Multilevel Modeling and Time-Varying Effect Modeling
Presenters: Stephanie Lanza and Michael Russell
Date: June 28 – 29, 2018
Venue: Penn State, University Park, PA
The goal of this two-day workshop was to provide attendees with the theoretical background and applied skills necessary to identify and address innovative and interesting research questions in intensive longitudinal data streams such as daily diary and ecological momentary assessment (EMA) data using multilevel modeling (MLM) and time-varying effect modeling (TVEM). By the end of the workshop, participants fit several multilevel and time-varying effect models in SAS and had the opportunity to fit and interpret preliminary models using their own data. Workshop time was spent in lecture, software demonstrations, computer exercises, and discussion. Participants were provided with a hard copy of all lecture notes, select computer exercises and output, and suggested reading lists for future reference. SAS software was used in the course, including native SAS procedures for analyzing multilevel models (PROC MIXED and PROC GLIMMIX) and the SAS TVEM macro, a downloadable supplement to SAS developed at the Penn State Methodology Center. Participants also applied the concepts learned in class to their own data, and the presenters were available for consultation during that period.
Basic familiarity with linear and logistic regression and the SAS software will be helpful.
Participants were strongly encouraged to bring a laptop so that they can conduct the computer exercises and analyze their own data. To conduct analyses at the workshop, SAS Version 9 for Windows needed to be installed on the laptop prior to arrival. In addition, approximately one week prior to the workshop participants were sent an email requesting that they download the TVEM SAS macro suite. Participants needed to verify that any data use agreements permited them to bring their own data to the workshop. Simulated data was made available to those who do not bring their own data.
— Conceptual introduction to multilevel modeling (MLM) and time-varying effect modeling (TVEM)
— Two-level MLM for daily diary and ecological momentary assessment (EMA) data
— Extension to three-level MLM for EMA data
— TVEM for EMA data: overview and applications (time of day, time relative to event, time since treatment) — Analyses using participants’ own data, presenters available for consultation
The workshop is complete. Please check back in 2019. Enrollment was limited to 40 participants to maintain an informal atmosphere and to encourage interaction between and among the presenters and participants. We gave priority to individuals who are involved in drug abuse 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 was directly and immediately relevant to their current work. We also aimed to maximize geographic and minority representation. The application window has closed. Once accepted, participants were emailed instructions about how to register. The registration fee of $395 for the two-day Institute covered all instruction, program materials, and breakfast and lunch each day. A block of rooms at the Nittany Lion Inn was available for lodging. Participants were encouraged to bring their own laptop computers for conducting exercises. Review our refund, access, and cancellation policies.
Stephanie Lanza, Ph.D.
Professor of Biobehavioral Health, Director of the Edna Bennett Pierce Prevention Research Center, Principal Investigator at The Methodology Center, Penn State Dr. Lanza has a background in research methods, human development, and substance use and comorbid behaviors, with more than 100 papers appearing in top methodological and applied journals. She is co-author of the book Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences and led the development of PROC LCA & PROC LTA, SAS procedures for fitting latent class and latent transition models. Her methodological research interests include advances in finite mixture modeling and time-varying effect modeling to address innovative research questions in behavioral research, particularly those best addressed using intensive longitudinal data. She is passionate about disseminating these methods to health, behavioral, and social science researchers and has organized many NIH-funded dissemination conferences, taught more than 30 intensive hands-on workshops, and written tutorial articles to enable applied researchers to use the latest methods in their own work.
Michael Russell, Ph.D.
Assistant Professor of Biobehavioral Health, Investigator at The Methodology Center, Penn State Dr. Russell’s research is focused on understanding the connections between stress, affect, and health behaviors in day-to-day life using advanced statistical modeling (multilevel and time-varying effect modeling) and ambulatory assessment methods (daily diaries, ecological momentary assessments (EMA), and wearable biosensors). He is currently leading a data collection effort that combines EMA and wearable biosensors for alcohol intoxication to understand the causes and consequences of young-adult heavy drinking episodes in daily life. Dr. Russell has a strong commitment to teaching and mentoring other health researchers in advanced analytic methods, as evidenced by numerous invited talks and workshops focused on advanced MLM, TVEM, and the analysis of intensive longitudinal data. His work has been published in a variety of top journals, including Annals of Behavioral Medicine, Development and Psychopathology, Journal of Adolescent Health, Prevention Science, Drug and Alcohol Dependence, and Psychology of Addictive Behaviors.
The Pennsylvania State University, University Park campus
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.
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