Estimation of Time-Unstructured Nonlinear Mixed-Effects Mixture Models

Sarfaraz Serang, Kevin Grimm, John J. McArdle

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Change over time often takes on a nonlinear form. Furthermore, change patterns can be characterized by heterogeneity due to unobserved subpopulations. Nonlinear mixed-effects mixture models provide one way of addressing both of these issues. This study attempts to extend these models to accommodate time-unstructured data. We develop methods to fit these models in both the structural equation modeling framework as well as the Bayesian framework and evaluate their performance. Simulations show that the success of these methods is driven by the separation between latent classes. When classes are well separated, a sample of 200 is sufficient. Otherwise, a sample of 1,000 or more is required before parameters can be accurately recovered. Ignoring individually varying measurement occasions can also lead to substantial bias, particularly in the random-effects parameters. Finally, we demonstrate the application of these techniques to a data set involving the development of reading ability in children.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalStructural Equation Modeling
DOIs
StateAccepted/In press - Jul 2 2016

Keywords

  • growth mixture model
  • longitudinal
  • mixed-effects model
  • nonlinear

ASJC Scopus subject areas

  • Modeling and Simulation
  • Decision Sciences(all)
  • Economics, Econometrics and Finance(all)
  • Sociology and Political Science

Fingerprint Dive into the research topics of 'Estimation of Time-Unstructured Nonlinear Mixed-Effects Mixture Models'. Together they form a unique fingerprint.

  • Cite this