Clustering of longitudinal data: A tutorial on a variety of approaches

TL;DR

This paper provides a comprehensive tutorial on various methods for clustering longitudinal data, including GBTM, GMM, and KML, highlighting their strengths, limitations, and extensions, especially for intensive data.

Contribution

It offers a clear overview and practical guidance on multiple longitudinal clustering methods, including recent developments and applications to intensive data.

Findings

Methods are demonstrated on synthetic data using R packages.

The tutorial discusses strengths and limitations of each approach.

Extensions for intensive longitudinal data are considered.

Abstract

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.