Identifying typical trajectories in longitudinal data: modelling strategies and interpretations.

European Journal of Epidemiology
Moritz HerleBianca L De Stavola

Abstract

Individual-level longitudinal data on biological, behavioural, and social dimensions are becoming increasingly available. Typically, these data are analysed using mixed effects models, with the result summarised in terms of an average trajectory plus measures of the individual variations around this average. However, public health investigations would benefit from finer modelling of these individual variations which identify not just one average trajectory, but several typical trajectories. If evidence of heterogeneity in the development of these variables is found, the role played by temporally preceding (explanatory) variables as well as the potential impact of differential trajectories may have on later outcomes is often of interest. A wide choice of methods for uncovering typical trajectories and relating them to precursors and later outcomes exists. However, despite their increasing use, no practical overview of these methods targeted at epidemiological applications exists. Hence we provide: (a) a review of the three most commonly used methods for the identification of latent trajectories (growth mixture models, latent class growth analysis, and longitudinal latent class analysis); and (b) recommendations for the identific...Continue Reading

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Citations

Apr 23, 2020·Muscle & Nerve·Hani Kushlaf
Aug 18, 2020·JAMA Network Open·Ahmed ElhakeemDeborah A Lawlor
Mar 23, 2021·Clinical Nutrition ESPEN·Moritz HerleDaisy Fancourt
Apr 8, 2021·International Journal of Obesity : Journal of the International Association for the Study of Obesity·Jessica A KerrMelissa Wake
May 2, 2021·Cognition·Ava GuezFranck Ramus
Jun 16, 2021·Scientific Reports·Diego Yacamán-MéndezAnton Lager

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Datasets Mentioned

BETA
GMM-1

Software Mentioned

LCGA
Mplus
mathop
R
Stata
SEM
lcmm

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