Classifying developmental trajectories over time should be done with great caution: a comparison between methods

Objective: In the analysis of data from longitudinal cohort studies, there is a growing interest in the analysis of developmental trajectories in subpopulations of the cohort under study. There are different advanced statistical methods available to analyze these trajectories, but in the epidemiologic literature, most of those are never used. The purpose of the present study is to compare five statistical methods to detect developmental trajectories in a longitudinal epidemiological data set.

Study design and setting: All five statistical methods (K-means clustering, a "two-step" approach with mixed modeling and K-means clustering, latent class analysis [LCA], latent class growth analysis [LCGA], and latent class growth mixture modeling [LCGMM]) were performed on a real-life data set and two manipulated data sets. The first manipulated data set contained four different linear developments over time, whereas the second contained two linear and two quadratic developments.

Results: For the real-life data set, all five classification methods revealed comparable trajectories. Regarding the manipulated data sets, LCGA performed best in detecting linear trajectories, whereas none of the methods performed well in detecting a combination of linear and quadratic trajectories. Furthermore, the optimal solution for LCA and LCGA contained more classes compared with LCGMM.

Conclusion: Although LCGA and LCGMM seem to be preferable above the more simple methods, all classification methods should be applied with great caution.