The impact of covariance misspecification in group-based trajectory models for longitudinal data with non-stationary covariance structure

Christopher E. Davies, Gary F.V. Glonek, Lynne C. Giles

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1982-1991
Number of pages10
JournalStatistical Methods in Medical Research
Volume26
Issue number4
Early online date17 Aug 2015
DOIs
Publication statusPublished - 1 Aug 2017
Externally publishedYes

Keywords

  • Covariance
  • group-based trajectory modelling
  • longitudinal data
  • mixture models
  • model misspecification

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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