The impact of covariance misspecification in group-based trajectory models for longitudinal data with non-stationary covariance structure. (August 2017)

• Record Type: Journal Article
• Authors: Davies, Christopher E; Glonek, Gary FV; Giles, Lynne C
• Other Names: Davidian Marie guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 26:Number 4(2017)
• Page Start: 1982
• Page End: 1991
• Publication Date: 2017-08
• Subjects: Covariance -- model misspecification -- mixture models -- longitudinal data -- group-based trajectory modelling; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280215598806
• Languages: English
• ISSNs: 0962-2802

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.