195501 Gauging Growth Trajectories of Two Related Outcomes Simultaneously Using Latent Growth Modeling

Monday, November 9, 2009: 3:10 PM

Jichuan Wang, PhD , Epidemiology and Biostatistics, School of Medicine, The George Washington University, Washington, DC
This study demonstrates how to gauge growth trajectories of two related outcomes simultaneously using the parallel latent growth models (LGM). A 30-month period longitudinal study data on crack-cocaine use and BDI-defined depression scores were used for the modeling. We first evaluated the form and determinants of the growth trajectories and the association between the trajectories of the two outcomes. Then, we scrutinized the changes in each outcome in specific time periods of interest during the entire observation period. Differences in outcome change in specific sub-time periods between ethnic groups were tested. The standard errors of the model predicted mean outcome changes in specific time periods were estimated via both the delta method and bootstrapping. Mplus and its RUNALL Utility program were applied for the modeling.

Learning Objectives:
1) Describe the fundamentals of parallel latent growth model (LGM) 2) Discuss and demonstrate how to use the delta method and bootstrapping to scrutinize outcome changes

Keywords: Statistics, Change

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I received my PhD from Cornell University in 1990 and have been doing studies on public health over 18 years.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.