Learning Activity: Latent Class Analysis Workshop

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September 21, 2015, 00:00
to: September 22, 2015, 00:00
Team: Events, Training & News

CHIMb.ca
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Learning Activity: Latent Class Analysis Workshop

Team: Events, Training & News

September 21, 2015, 00:00
to: September 22, 2015, 00:00

Description


To register for this workshop, please click HERE.

Latent class models are a powerful, flexible approach to finding and describing otherwise undetectable subpopulations whose behaviors differ with respect to clinically important features. Advances in statistical methodology and computational power have made it a viable tool with growing popularity. Applications include topics such as consumer shopping patterns, gambling and substance abuse risks, depression and scholastic ability.

The goal of this workshop is to:

     1. Provide an introduction to latent class analysis from an applied perspective
     2. Demonstrate the PROC LCA (Latent Class Analysis Procedure) routine and related SAS macros for model estimation

By the end of the workshop, participants will be able to compare LCA with related models; interpret LCA output and make conclusions from it; understand the limitations of traditional LCA; and employ extensions of LCA to include covariates, parameter constraints, and outcomes.

Session 1. Monday, September 21, 1-4pm
Foundational concepts of traditional LCA will be gently discussed, including the mathematical model and its estimation routines. The interpretation of parameter estimates and the comparison of competing models will be of focus. PROC LCA will be demonstrated alongside the many applied examples.  

Session 2: Tuesday, September 22, 1-4pm
The basic LCA model will be extended to include covariates and distal outcomes, as well as the investigation of parameter invariance across multiple groups of interest. Time permitting, other advanced topics and computer programs may be discussed.

Requirements:
Experience with latent modeling is not required, however knowledge of generalized linear regression modeling and categorical data analysis is recommended. Some familiarity with SAS may be an asset, but is not required.

About the Instructor:
Mr. Brenden Dufault is a full-time biostatistical consultant with the George and Fay Yee Centre for Healthcare Innovation. He has 6 years of experience with the design and analysis of studies in the biological sciences, immunology, population health, nursing, psychology, and epidemiology. His interests include mixed-effects models, latent mixture models, and high-dimensional data mining algorithms.

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