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latrend: A Framework for Clustering Longitudinal Data (2402.14621v1)

Published 22 Feb 2024 in cs.LG and stat.ML

Abstract: Clustering of longitudinal data is used to explore common trends among subjects over time for a numeric measurement of interest. Various R packages have been introduced throughout the years for identifying clusters of longitudinal patterns, summarizing the variability in trajectories between subject in terms of one or more trends. We introduce the R package "latrend" as a framework for the unified application of methods for longitudinal clustering, enabling comparisons between methods with minimal coding. The package also serves as an interface to commonly used packages for clustering longitudinal data, including "dtwclust", "flexmix", "kml", "lcmm", "mclust", "mixAK", and "mixtools". This enables researchers to easily compare different approaches, implementations, and method specifications. Furthermore, researchers can build upon the standard tools provided by the framework to quickly implement new cluster methods, enabling rapid prototyping. We demonstrate the functionality and application of the latrend package on a synthetic dataset based on the therapy adherence patterns of patients with sleep apnea.

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Citations (2)
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Summary

  • The paper introduces a comprehensive R framework that unifies 18 clustering methods for analyzing longitudinal data with minimal coding.
  • It provides a standardized interface for specifying, estimating, comparing, and evaluating diverse methods, thereby streamlining temporal data analysis.
  • Its extensible design enables rapid prototyping and the discovery of multiple common trends, enhancing insights across various research domains.

Unveiling latrend: A Comprehensive Framework for Clustering Longitudinal Data in R

Introduction to latrend

In recent developments within the field of Longitudinal Data Analysis, the latrend package emerges as a unifying framework designed for clustering longitudinal patterns. This R package not only facilitates the application of various longitudinal clustering methods but also simplifies method comparison and evaluation with minimal coding effort. With an eye for detail, this framework provides a novel avenue for researchers to explore the heterogeneity in longitudinal datasets through an array of clustering methods borrowed from various fields of research.

Key Features of latrend

The latrend package stands out by offering a cohesive interface to a multitude of existing R packages such as dtwclust, flexmix, kml, lcmm, mclust, mixAK, and mixtools, which are renowned for clustering longitudinal data. This enables a seamless exploration of different clustering methods, ensuring a comprehensive analysis. Here are some of the noteworthy features of latrend:

  • Unified Application: latrend offers a standardized approach to specify, estimate, select, compare, and evaluate longitudinal cluster methods. This promotes efficiency and consistency across analyses.
  • Minimal Coding: By abstracting the complexity involved in comparing methods from different packages, latrend enables users to focus on analysis without the burden of intricate coding.
  • Rapid Prototyping: The framework is designed to be extensible, allowing users to quickly implement new cluster methods, thus fostering innovation and experimentation.
  • Comparative Analysis: latrend not only facilitates the application of multiple methods but also supports method comparison through a standardized set of metrics, aiding in the selection of the most appropriate method for the dataset at hand.

Methodological Insights

latrend adopts a nuanced approach to clustering longitudinal data by emphasizing the representation of data variability in terms of multiple common trends. This is a departure from traditional methods that often represent longitudinal datasets by a single trend. The package introduces the capability to identify and analyze these multiple trends in a data-driven manner, enhancing the understanding of subject variability over time.

The framework is designed with extendibility in mind, supporting a total of 18 methods for longitudinal clustering at the time of its introduction. This inclusivity ensures that users have access to a broad spectrum of clustering approaches, ranging from distance-based to regression-based methods, each suitable for different types of longitudinal data patterns.

Practical Implications and Future Directions

The practical implications of the latrend package are vast, particularly in fields where longitudinal studies are prevalent, such as criminology, psychology, and medicine. By enabling a detailed exploration of subgroup patterns over time, latrend can significantly contribute to understanding phenomena such as recidivism behavior, the development of antisocial behavior among adolescents, and patterns of medication adherence among patients.

Looking ahead, the latrend framework has the potential to be expanded further to include support for multitrajectory modeling and categorical outcomes. Such enhancements would broaden the scope of latrend, making it even more versatile and applicable to a wider range of longitudinal datasets.

Concluding Thoughts

The introduction of the latrend package marks a significant advancement in the analysis of longitudinal data. By offering a comprehensive framework that simplifies the application and comparison of various longitudinal clustering methods, latrend stands to be an invaluable tool for researchers seeking to uncover the underlying trends in their longitudinal datasets. As the package continues to evolve, it is poised to unlock new possibilities in the field of longitudinal data analysis, equipped with the potential to drive forward scientific discovery across diverse fields of paper.

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