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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Vydané v:	Communications in statistics. Simulation and computation Ročník 52; číslo 3; s. 621 - 648
Hlavní autori:	Den Teuling, N. G. P., Pauws, S. C., van den Heuvel, E. R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Philadelphia Taylor & Francis 04.03.2023 Taylor & Francis Ltd
Predmet:	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
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