242595 Examining Longitudinal Patterns of Recovery: A Growth Mixture Modeling Approach using Posterior Mode Estimation

Wednesday, November 2, 2011: 11:30 AM

Yoon Soo Park, PhD , Research and Development, Educational Testing Service, Princeton, NJ
Tasha Stehling-Ariza, MPH , National Center for Disaster Preparedness, Columbia University, New York, NY
David M. Abramson, PhD MPH , National Center for Disaster Preparedness, Columbia University, New York, NY
In most approaches to analysis of change, population homogeneity is assumed. However, in settings such as disaster recovery, there are inherent differences among individuals that make the population more heterogeneous. For example, there are individuals that are more resistant to disasters, and there are individuals that are more vulnerable. Using this framework, subpopulations that describe an individual's pattern of recovery can be classified into several latent groups (e.g., resistant, resilient, chronically dysfunctional). Examining recovery as a change over time is meaningful, because they reveal subgroup differences in developmental processes, which may be sensitive to external social factors and various interventions.

This study examines the use of a growth mixture model (GMM; Muthén & Muthén, 2004) that empirically classifies individuals into latent subgroups of recovery based on their recovery growth trajectories; this method estimates separate parameters of the intercept and the slope for each latent subpopulation. However, it has been noted in the literature that there are often boundary problems in numerical computation and estimation when applying maximum likelihood estimation (MLE) for latent class models (Galindo-Garre & Vermunt, 2006) such as the GMM. This problem can be prevented when a Bayesian posterior mode (PME) estimation is used (McLachlan & Krishnan, 2008). This study focuses on comparing the PME approach to the traditional MLE in GMM. Simulations will examine the recovery of parameters and classification; an empirical analysis using a longitudinal cohort of displaced households from Hurricane Katrina will also be used to demonstrate the advantages of this method.

Learning Areas:
Biostatistics, economics
Public health or related research
Social and behavioral sciences

Learning Objectives:
- Identify differences in estimation techniques such as posterior mode estimation and maximum likelihood for detecting changes in populations - Explain methods for longitudinal analysis using finite mixture distributions - Demonstrate the technique for describing trajectories of recovery in a displaced population from a disaster

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am the Data Manager and Analyst at the National Center for Disaster Preparedness at Columbia University. We study patterns of recovery from displaced populations. I have co-authored several studies on disaster recovery. I am also a doctoral candidate in measurement and statistics at Columbia University. The topic of latent variable modeling and estimation is within the area of my dissertation.
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.