Sunday, October 23, 2016: 2:00 PM - 5:30 PM
Oak 1, Second Floor (Westin Bayshore Vancouver)
Course Type: Half Day
Course Level: Beginner

Background: This short course will make Structural Equation Modeling (SEM) accessible to a wide audience of researchers across many disciplines. SEM is a very general and powerful technique to link conceptual models, path diagrams, factor analysis and other mathematical models. These techniques allow for 1) the combination of continuous, categorical and latent and observed variables; 2) modeling of causal relationships including multiple direct and indirect effects in a single analysis; 3) cutting-edge techniques for model selection and comparison; 4) compact representation of cost-utility problems; 5) dynamic updates to model predictions as new clinical measures are obtained; and 6) the ability to break down complex causal structures into many smaller, more manageable local models which can then be combined together. These advantages are particularly applicable to both theoretical and applied research problems in medical decision making.

Format Requirements: Participants will experience a mixture of lecture and discussion. We will introduce basic concepts and vocabulary of SEM and Bayesian networks (BN), give real-world examples and conduct sample analyses using SEM software. No prior knowledge of SEM or BN is required. Participants with a basic understanding of statistics will benefit most from this course.

Description and Objectives:

We present a basic overview of SEM and BN principles, some common nomenclature, diagrams, a tiny bit of algebra (with few Greek letters!), real world examples, and a glimpse into more advanced SEM techniques such as measurement invariance testing, latent growth curve modeling and how Bayesian networks are related to SEM. In this course, you will:
  • Enrich your way of thinking about certain medical decision making problems;
  • Learn fundamental concepts underpinning SEM and BN models;
  • Gain knowledge of resources and techniques for causal modeling;
  • Be introduced to software for implementing SEM and BN analyses;
  • Be able to interpret results of advanced causal modeling techniques;
  • Understand advantages of SEMs and BNs over traditional statistical models.

Whether you want to know how to critique a SEM or BN article, or want to engage a few SEM researchers in some feisty methods discussions, sign up for this course...we'd love to visit with you.

Course Director:

Joseph J. Sudano, PhD
Case Western Reserve University at The MetroHealth System
Assistant Professor of Medicine, Epidemiology and Biostatistics
Center for Health Care Research and Policy

Dr. Sudano is a medical sociologist, senior health services researcher and Co-Director of the Social and Behavioral Science Unit in the Center for Health Care Research and Policy, Case Western Reserve University at The MetroHealth System. He is also Assistant Professor in the Department of Medicine at CWRU School of Medicine. His research interests include ethnic disparities in health; health related survey data collection and analysis; social determinants of health; measurement equivalence in cross-cultural health status measurement; and structural equation modeling in health outcomes research.

Course Faculty:

Adam T. Perzynski, PhD
Case Western Reserve University at MetroHealth
Assistant Professor of Medicine
Center for Health Care Research and Policy

Dr. Perzynski is Assistant Professor of Medicine and Sociology in the MetroHealth Department of Medicine at Case Western Reserve University. He is a sociologist specializing in medical sociology, life course studies, and research methods. His methodology expertise spans the continuum from ethnography to structural equation modelling. In 2014, he founded the nation's first Patient Centered Media Lab (, focused on aligning the power of health technology innovation with patient values and preferences. Teams from this lab have produced award-winning software prototypes that combine primary care and public health data sources to place health risk information in the hands of patients, health care providers and public health officials. His recent work has combined data from public and community sources with data from the electronic health record to evaluate how social factors influence health behavior and health outcomes.

Jarrod E. Dalton, PhD
Cleveland Clinic
9500 Euclid Avenue
Quantitative Health Sciences and Outcomes Research

Dr. Dalton is an Assistant Professor of Medicine in the Cleveland Clinic Lerner College of Medicine at Case Western Reserve University and a member of the Department of Quantitative Health Sciences at Cleveland Clinic. His research interests include medical decision making; mathematical, ecological and systems-based modeling; machine learning and artificial intelligence; causal network modeling; health economics; information theory; and statistical software development. Currently, he is investigating environmental and behavioral aspects to the progression and management of atherosclerotic disease including the development of new methods for validating dynamic forecasting models.