A comparison of methods for clustering longitudinal data with slowly changing trends

• Published in: Communications in statistics. Simulation and computation Vol. 52; no. 3; pp. 621 - 648
• Main Authors: Den Teuling, N. G. P., Pauws, S. C., van den Heuvel, E. R.
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 04.03.2023; Taylor & Francis Ltd
• Subjects: Clustering; Datasets; Error analysis; Group-based trajectory modeling; Growth mixture modeling; Intensive longitudinal data; Latent class analysis; Latent-class trajectory modeling; Longitudinal clustering; Modelling; Performance evaluation; Polynomials; Simulation study

Longitudinal clustering provides a detailed yet comprehensible description of time profiles among subjects. With several approaches that are commonly used for this purpose, it remains unclear under which conditions a method is preferred over another method. We investigated the performance of five me...