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A Life Course Perspective on Health Trajectories and TransitionsThe Analysis of Individual Health Trajectories Across the Life Course: Latent Class Growth Models Versus Mixed Models

A Life Course Perspective on Health Trajectories and Transitions: The Analysis of Individual... [When analysing individual trajectories across the life course, the fact that repeated observations of individuals are not independent (i.e. correlated) should be taken into account. Several techniques to do this are available, such as mixed models, MM and latent growth models, LGM. These models can also elegantly incorporate different stages of the life course, including childhood, adolescence and adulthood in the modelling process. MM do so by the inclusion of a ‘time’ variable denoting each stage in the model and LGM can be conducted in a piecewise manner, where each ‘piece’ represents a life course stage. Moreover, both techniques can further be extended to allow for possible heterogeneity in health trajectory (shape), but do so in different ways. MM can include random slopes to account for heterogeneity in growth; LGM can be extended into latent class growth models to allow for the possible revelation of subgroups of individuals determined by the data with distinct health trajectories across the life course.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Life Course Perspective on Health Trajectories and TransitionsThe Analysis of Individual Health Trajectories Across the Life Course: Latent Class Growth Models Versus Mixed Models

Part of the Life Course Research and Social Policies Book Series (volume 4)
Editors: Burton-Jeangros, Claudine; Cullati, Stéphane; Sacker, Amanda; Blane, David

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References (40)

Publisher
Springer International Publishing
Copyright
© The Editor(s) and the Author(s) 2015
ISBN
978-3-319-20483-3
Pages
179 –195
DOI
10.1007/978-3-319-20484-0_9
Publisher site
See Chapter on Publisher Site

Abstract

[When analysing individual trajectories across the life course, the fact that repeated observations of individuals are not independent (i.e. correlated) should be taken into account. Several techniques to do this are available, such as mixed models, MM and latent growth models, LGM. These models can also elegantly incorporate different stages of the life course, including childhood, adolescence and adulthood in the modelling process. MM do so by the inclusion of a ‘time’ variable denoting each stage in the model and LGM can be conducted in a piecewise manner, where each ‘piece’ represents a life course stage. Moreover, both techniques can further be extended to allow for possible heterogeneity in health trajectory (shape), but do so in different ways. MM can include random slopes to account for heterogeneity in growth; LGM can be extended into latent class growth models to allow for the possible revelation of subgroups of individuals determined by the data with distinct health trajectories across the life course.]

Published: May 19, 2015

Keywords: Bayesian Information Criterion; Random Slope; Individual Trajectory; Latent Growth Model; Health Trajectory

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