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

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...

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Veröffentlicht in:	Communications in statistics. Simulation and computation Jg. 52; H. 3; S. 621 - 648
Hauptverfasser:	Den Teuling, N. G. P., Pauws, S. C., van den Heuvel, E. R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Philadelphia Taylor & Francis 04.03.2023 Taylor & Francis Ltd
Schlagworte:	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
ISSN:	0361-0918, 1532-4141
Online-Zugang:	Volltext
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