Joint Trajectory Model for Parallel-Process Data with Distal Outcome

• Activity Number: 317
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: SSC
• Author(s): Depeng Jiang*+ and Robert Tate
• Companies: University of Manitoba and University of Manitoba
• Keywords: trajectory model ; growth mixture modeling ; multivariate longitudinal data

Abstract

This study proposes a general growth mixture model for jointly analyzing multivariate longitudinal data with distal outcome. Growth mixture modeling represents unobserved heterogeneity between subjects in their development using both random effects (e.g., Laird and Ware, 1982) and finite mixtures (e.g., McLachlan and Peel, 2000). To introduce the joint trajectory model, we begin by exploring separate trajectory models for each outcome variable, respectively. We determine the number of trajectory classes for each outcome variable considering goodness of fit, parsimony and stability. Then joint trajectory model was fitted to jointly modeling the parallel-process data (e.g., trajectories of mental functioning and trajectories of physical functioning). Finally, the distal outcome variable was added to the joint trajectory model and the general growth mixture model framework was presented. Using the Data from the Manitoba Follow-up Study concerns joint trajectories of mental and physical functioning and mortality outcome serve as an illustration.