Identification of developmental trajectory classes: Comparing three latent class methods using simulated and real data.

INTRODUCTION

Several statistical methods are available to identify developmental trajectory classes, but it is unclear which method is most suitable. The aim of this study was to determine whether latent class analysis, latent class growth analysis or growth mixture modeling was most appropriate for identifying developmental trajectory classes.

METHODS

We compared the three methods in a simulation study in several scenarios, which varied regarding e.g. sample size and degree of separation between classes. The simulation study was replicated with a real data example concerning anxiety/depression symptoms measured over 6 time points in the Tracking Adolescent Individuals' Lives Survey (TRAILS, N = 2227).

RESULTS

Growth mixture modeling was least biased or equally biased compared to latent class analysis and latent class growth analysis in all scenarios. In TRAILS, the shapes of the trajectories were rather similar over the three methods, but class sizes differed slightly. A 4-class growth mixture model performed best, based on several fit indices, interpretability and clinical relevance.

CONCLUSIONS

Growth mixture modeling seems most suitable to identify developmental trajectory classes.