Every week, we select a recently published Open Access article to feature. This Tutorial in Biostatistics is  from Statistics in Medicine and provides an overview of several commonly used approaches to clustering multiple longitudinal features. 

The article’s abstract is given below, with the full article available to read here. 

Lu, Z, Ahmadiankalati, M, Tan, Z. Joint clustering multiple longitudinal features: A comparison of methods and software packages with practical guidance. Statistics in Medicine. 2023; 1–28. doi: 10.1002/sim.9917

Clustering longitudinal features is a common goal in medical studies to identify distinct disease developmental trajectories. Compared to clustering a single longitudinal feature, integrating multiple longitudinal features allows additional information to be incorporated into the clustering process, which may reveal co-existing longitudinal patterns and generate deeper biological insight. Despite its increasing importance and popularity, there is limited practical guidance for implementing cluster analysis approaches for multiple longitudinal features and evaluating their comparative performance in medical datasets. In this paper, we provide an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software. These methods can be broadly categorized into two categories, namely model-based (including frequentist and Bayesian) approaches and algorithm-based approaches. To evaluate their performance, we compare these approaches using real-life and simulated datasets. These results provide practical guidance to applied researchers who are interested in applying these approaches for clustering multiple longitudinal features. Recommendations for applied researchers and suggestions for future research in this area are also discussed.