title: Latent class analysis was accurate but sensitive in data simulations.
abstract: OBJECTIVE
  Latent class methods are increasingly being used in analysis of developmental trajectories. A recent simulation study by Twisk and Hoekstra (2012) suggested caution in use of these methods because they failed to accurately identify developmental patterns that had been artificially imposed on a real data set. This article tests whether existing developmental patterns within the data set used might have obscured the imposed patterns.
  
  
  METHODS
  Data were simulated to match the latent class pattern in the previous article, but with varying levels of randomly generated variance, rather than variance carried over from a real data set. Latent class analysis (LCA) was then used to see if the latent class structure could be accurately identified.
  
  
  RESULTS
  LCA performed very well at identifying the simulated latent class structure, even when the level of variance was similar to that reported in the previous study, although misclassification began to be more problematic with considerably higher levels of variance.
  
  
  CONCLUSIONS
  The failure of LCA to replicate the imposed patterns in the previous study may have been because it was sensitive enough to detect residual patterns of population heterogeneity within the altered data. LCA performs well at classifying developmental trajectories.
authors: MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, 200 Renfield Street, Glasgow, G2 3QB, United Kingdom. Electronic address: michael.green@glasgow.ac.uk.
authors: Michael J
authors: MJ
authors: Green
mesh_terms: D000704:Analysis of Variance; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015203:Reproducibility of Results
keywords: Development; Heterogeneity; Latent class analysis; Longitudinal; Simulations; Trajectories