Scalable and robust latent trajectory class analysis using artificial likelihood

Latent trajectory class analysis is a powerful technique to elucidate the structure underlying population heterogeneity. The standard approach relies on fully parametric modeling and is computationally impractical when the data include a large collection of non-Gaussian longitudinal features. We introduce a new approach, the first based on artificial likelihood concepts, that avoids undue modeling assumptions and is computationally tractable. We show that this new method provides reliable estimates of the underlying population structure and is from 20 to 200 times faster than conventional methods when the longitudinal features are non-Gaussian. We apply the approach to explore subgroups among research participants in the early stages of neurodegeneration.