Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/94126
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Type: Journal article
Title: The impact of covariance misspecification in group-based trajectory models for longitudinal data with non-stationary covariance structure
Author: Davies, C.
Glonek, G.
Giles, L.
Citation: Statistical Methods in Medical Research, 2017; 26(4):1982-1991
Publisher: SAGE Publications
Issue Date: 2017
ISSN: 0962-2802
1477-0334
Statement of
Responsibility: 
Christopher E Davies, Gary FV Glonek, and Lynne C Giles
Abstract: One purpose of a longitudinal study is to gain a better understanding of how an outcome of interest changes among a given population over time. In what follows, a trajectory will be taken to mean the series of measurements of the outcome variable for an individual. Group-based trajectory modelling methods seek to identify subgroups of trajectories within a population, such that trajectories that are grouped together are more similar to each other than to trajectories in distinct groups. Group-based trajectory models generally assume a certain structure in the covariances between measurements, for example conditional independence, homogeneous variance between groups or stationary variance over time. Violations of these assumptions could be expected to result in poor model performance. We used simulation to investigate the effect of covariance misspecification on misclassification of trajectories in commonly used models under a range of scenarios. To do this we defined a measure of performance relative to the ideal Bayesian correct classification rate. We found that the more complex models generally performed better over a range of scenarios. In particular, incorrectly specified covariance matrices could significantly bias the results but using models with a correct but more complicated than necessary covariance matrix incurred little cost
Keywords: Covariance; model misspecification; mixture models; longitudinal data; group-based trajectory modelling
Rights: © The Author(s) 2015
RMID: 0030033347
DOI: 10.1177/0962280215598806
Grant ID: http://purl.org/au-research/grants/nhmrc/465455
http://purl.org/au-research/grants/nhmrc/465437
http://purl.org/au-research/grants/nhmrc/627033
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