In precision medicine, vast amounts of naturally occurring data are used to derive evidence-based knowledge. Recognizing distinct trajectories, identifying potential predictors, and making data-driven decisions are areas of growing interest. Mixture-based mixed effects models, implemented mainly in R and Mplus are useful for this. While theoretically sound, these approaches often fail to fully address the problem due to computational complexities. Linear mixed effects models ballpark curves of irregularly spaced longitudinal data with optimum precision. Random effects (REs), assumed to be distributed as multivariate normal (MVN), vary across individuals accounting for sources of heterogeneity. To identify presence of subgroups, we apply post-hoc Gaussian finite mixtures on empirical BLUPs assuming a mixture of MVN of REs to classify trajectories. Resulting ellipsoids can vary by center or by features- size, volume and orientation that are determined by eigenvalue decomposition of covariance matrix. This study used simulations to extend previous application of real data to evaluate the classification performance of this method compared with that of existing methods.