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A comparison of longitudinal modelling approaches: Alcohol and cannabis use from adolescence to young adulthood

Version 2 2024-06-05, 11:10
Version 1 2019-06-18, 09:02
journal contribution
posted on 2024-06-05, 11:10 authored by Christopher GreenwoodChristopher Greenwood, George YoussefGeorge Youssef, KS Betts, Primrose LetcherPrimrose Letcher, J Mcintosh, Liz SpryLiz Spry, Delyse HutchinsonDelyse Hutchinson, Jacqui MacdonaldJacqui Macdonald, LJ Hagg, A Sanson, John ToumbourouJohn Toumbourou, Craig OlssonCraig Olsson
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.

History

Journal

Drug and Alcohol Dependence

Volume

201

Pagination

58-64

Location

Ireland

ISSN

0376-8716

eISSN

1879-0046

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2019, Elsevier B.V.

Publisher

ELSEVIER IRELAND LTD