Clustering of longitudinal data: A tutorial on a variety of approaches
This tutorial supports researchers in applying clustering techniques to longitudinal data, but it is incremental as it synthesizes existing methods without introducing new ones.
The paper provides a tutorial summarizing various methods for clustering longitudinal data, including group-based trajectory modeling, growth mixture modeling, and longitudinal k-means, and demonstrates their application on a synthetic dataset using R packages.
During the past two decades, methods for identifying groups with different trends in longitudinal data have become of increasing interest across many areas of research. To support researchers, we summarize the guidance from the literature regarding longitudinal clustering. Moreover, we present a selection of methods for longitudinal clustering, including group-based trajectory modeling (GBTM), growth mixture modeling (GMM), and longitudinal k-means (KML). The methods are introduced at a basic level, and strengths, limitations, and model extensions are listed. Following the recent developments in data collection, attention is given to the applicability of these methods to intensive longitudinal data (ILD). We demonstrate the application of the methods on a synthetic dataset using packages available in R.