Abstract:
|
The linear spline growth mixture model is a tool for analyzing longitudinal data that come from a mixture of at least two latent classes where the underlying trajectories within each class are nonlinear. For each latent class, it approximates complex patterns by attaching at least two linear pieces. It poses interesting statistical challenges, such as estimating the location of a change point (or knot), grouping individuals into latent classes, associating those latent groups to baseline characteristics, and analyzing data with individually-varying measurement occasions. We developed a two-step bilinear spline growth mixture model (BLSGMM) to cluster these linear piecewise individual trajectories and associate time-invariant covariates (TICs) into latent classes. Our simulation studies demonstrate that the proposed BLSGMM-TICs can cluster the nonlinear trajectories well and estimate the parameters unbiasedly, precisely, and exhibit appropriate confidence interval coverage. An empirical example using longitudinal math scores shows that the model helps identify latent groups of nonlinear trajectories and associate the classes with baseline characteristics.
|