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

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.

Identifying Novel Clusters of Patients With Prolonged Mechanical Ventilation Using Trajectories of Rapid Shallow Breathing Index

“…Each trajectory was represented by the coefficients of an individually fitted linear regression model. The trajectories are then clustered based on the coefficients using k-means clustering ( 15 , 16 ). The best number of clusters was determined by multiple metrics including log likelihood value, Bayesian information criterion (BIC), and Akaike's information criterion (AIC).…”