Citation

Greenwood CJ, Youssef GJ, Betts KS, Letcher P, Mcintosh J, Spry E, Hutchinson DM, Macdonald JA, Hagg LJ, Sanson A, Toumbourou JW, Olsson CA. Drug Alcohol Depend. 2019; 201: 58-64.

Affiliation

Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia; Murdoch Children's Research Institute, Centre for Adolescent Health, Melbourne, Australia; University of Melbourne, Department of Paediatrics, Royal Children's Hospital, Australia.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.drugalcdep.2019.05.001

PMID

31195345

Abstract

BACKGROUND: Modelling trajectories of substance use over time is complex and requires judicious choices from a number of modelling approaches. In this study we examine the relative strengths and weakness of latent curve models (LCM), growth mixture modelling (GMM), and latent class growth analysis (LCGA).

DESIGN: Data were drawn from the Australian Temperament Project, a 36-year-old community-based longitudinal study that has followed a sample of young Australians from infancy to adulthood across 16 waves of follow-up since 1983. Models were fitted on past month alcohol use (n = 1468) and cannabis use (n = 549) across six waves of data collected from age 13-14 to 27-28 years.

FINDINGS: Of the three model types, GMMs were the best fit. However, these models were limited given the variance of numerous growth parameters had to be constrained to zero. Additionally, both the GMM and LCGA solutions had low entropy. The negative binomial LCMs provided a relatively well-fitting solution with fewer drawbacks in terms of growth parameter estimation and entropy issues. In all cases, model fit was enhanced when using a negative binomial distribution.

CONCLUSIONS: Substance use researchers would benefit from adopting a complimentary framework by exploring both LCMs and mixture approaches, in light of the relative strengths and weaknesses as identified. Additionally, the distribution of data should inform modelling decisions.

Copyright © 2019 Elsevier B.V. All rights reserved.

Language: en

Keywords

Growth mixture model; Latent class growth analysis; Latent curve model; Negative binomial; Substance use