Group-Based Trajectory Modeling Extended to Account for Nonrandom Subject Attrition

• Activity Number: 351
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Amelia Haviland*+ and Daniel Nagin and Bobby Jones
• Companies: RAND Corporation and Carnegie Mellon University and Carnegie Mellon University
• Keywords: mixture model ; longitudinal data ; missing data ; attrition

Abstract

We report on an extension of group-based trajectory modeling to address non-random subject attrition that varies across trajectory groups. The effects of the model extension are explored in both simulated and real data. The analyses of simulated data establish that estimates of trajectory group size as measured by group membership probabilities can be badly biased by differential attrition rates across groups if the groups are initially not well separated. Differential attrition rates also imply that group sizes will change over time which, in turn, has important implications for using the model parameter estimates to make population-level projections. Analyses of longitudinal data on disability levels in a sample of very elderly individuals support both of these conclusions.