Authors: Bastian Pfeifer, Simon Grabner & Andrea Berghold from Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz and Markus Loecher from Berlin School of Economy and Law. DOI: https://doi.org/10.1186/s12874-026-02903-3

The proposed methods implemented within the R-package TAPIO, is freely available on GitHub.

Abstract

Purpose: Clustering longitudinal data is challenging, particularly when measurements are high-dimensional, irregularly sampled, or noisy. We aim to provide a flexible and interpretable framework for identifying meaningful temporal subgroups and their key features.

Methods: We introduce TAPIO, an ensemble-based clustering approach with longitudinal extensions, including longTAPIOtrajectories, longTAPIOsample, and longTAPIOMLD. These variants integrate dimension reduction and cluster-specific feature importance, allowing robust clustering of univariate and multivariate trajectories, as well as regularly and irregularly sampled longitudinal data.

Results: Simulation studies demonstrate that TAPIO accurately recovers cluster structure, identifies relevant features, and performs competitively with existing methods. longTAPIOtrajectories excels on regularly sampled data, while longTAPIOMLD outperforms alternatives for irregular measurements. Applications on a clinical cohort reveal patient subgroups with distinct survival patterns driven by key cardiac measures, and analyses of high-dimensional longitudinal proteomics data uncover molecularly distinct clusters with interpretable protein-level importance profiles.

Conclusion: TAPIO offers a scalable, interpretable framework for longitudinal clustering that accommodates complex multivariate trajectories and highdimensional data. Its ability to identify both meaningful clusters and their defining features has potential to advance patient stratification, biomarker discovery, and longitudinal data analysis in biomedical research.