TY - JOUR
T1 - A comparison of longitudinal modelling approaches
T2 - alcohol and cannabis use from adolescence to young adulthood
AU - Greenwood, C. J.
AU - Youssef, G. J.
AU - Betts, K. S.
AU - Letcher, P.
AU - Mcintosh, J.
AU - Spry, E.
AU - Hutchinson, D. M.
AU - Macdonald, J. A.
AU - Hagg, L. J.
AU - Sanson, A.
AU - Toumbourou, J. W.
AU - Olsson, C. A.
PY - 2019/8/1
Y1 - 2019/8/1
N2 - 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.
AB - 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.
KW - substance use
KW - latent curve model
KW - growth mixture model
KW - latent class growth analysis
KW - negative binomial
UR - http://www.scopus.com/inward/record.url?scp=85066941891&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DP130101459
UR - http://purl.org/au-research/grants/arc/DP160103160
UR - http://purl.org/au-research/grants/arc/DP180102447
UR - http://purl.org/au-research/grants/nhmrc/1082406
U2 - 10.1016/j.drugalcdep.2019.05.001
DO - 10.1016/j.drugalcdep.2019.05.001
M3 - Article
C2 - 31195345
AN - SCOPUS:85066941891
SN - 0376-8716
VL - 201
SP - 58
EP - 64
JO - Drug and Alcohol Dependence
JF - Drug and Alcohol Dependence
ER -